Distributed Inference: When One GPU Can’t Fit a Large Model

GPT-4, LLaMA-70B, Mixtral-8x22B… these large models have parameters ranging from hundreds of billions to trillions—a single GPU simply cannot hold them. Distributed inference technology emerged to enable multiple GPUs to work together, jointly completing inference tasks for ultra-large models.

Why Do We Need Distributed Inference?

The Single-Card Dilemma

Let’s do some math:

The most powerful consumer GPU (RTX 4090) only has 24GB of memory, and even professional A100s only have 80GB.

Conclusion: Large models must be “split” and deployed across multiple cards.

Three Strategies for Distributed Inference

Just as there are multiple ways to move a mountain, distributed inference has multiple strategies:

1. Tensor Parallelism (TP)

Idea: “Horizontally slice” a single computation task across multiple GPUs.

Analogy: A super-large math problem is split into parts, each person computes one part, and answers are combined at the end.

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          ┌──────────────────────────────────────┐
Original  │          A (4096 × 4096)             │
Matrix    └──────────────────────────────────────┘
                            ↓ Horizontal split
          ┌─────────────┐  ┌─────────────┐
GPU 0     │ A[:, :2048] │  │ A[:, 2048:] │  GPU 1
          └─────────────┘  └─────────────┘
                ↓                  ↓
            Compute Part1      Compute Part2
                ↓                  ↓
          └──────────── AllReduce ────────────┘
                            ↓
                      Final Result

Characteristics:

• Each layer computation requires GPU communication (AllReduce)
• High communication bandwidth requirements (needs NVLink or similar)
• Suitable for single-machine multi-GPU scenarios
• Can reduce latency (each card computes less)

2. Pipeline Parallelism (PP)

Idea: “Vertically slice” the model by layers, different GPUs handle different layers.

Analogy: Factory assembly line—each worker handles one process, products pass through sequentially.

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Input → [GPU 0: Layers 1-8] → [GPU 1: Layers 9-16] → [GPU 2: Layers 17-24] → Output

     Time →
GPU 0: [Batch1 L1-8] [Batch2 L1-8] [Batch3 L1-8] ...
GPU 1:              [Batch1 L9-16] [Batch2 L9-16] ...
GPU 2:                            [Batch1 L17-24] ...

Characteristics:

• Less inter-GPU communication (only activations passed between stages)
• “Pipeline bubbles” exist (some GPUs idle waiting)
• Suitable for cross-machine deployment
• High throughput, but single-request latency may increase

3. Data Parallelism (DP)

Idea: Each GPU has a complete model replica, each processing different requests.

Analogy: Opening multiple franchise stores—each store can provide complete service.

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Request1 → GPU 0 (complete model) → Result1
Request2 → GPU 1 (complete model) → Result2
Request3 → GPU 2 (complete model) → Result3

Characteristics:

• Simple implementation
• No inter-GPU communication overhead (during inference)
• Prerequisite: Single card can fit the entire model
• Throughput scales linearly

Hybrid Parallel Strategies

In production, multiple parallel strategies are often combined:

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                 Data Parallel (DP=2)
     ┌─────────────────────────────────────────────┐
     │                                             │
     ▼                                             ▼
┌─────────┐                                   ┌─────────┐
│ Replica1│                                   │ Replica2│
└─────────┘                                   └─────────┘
     │                                             │
     ▼                                             ▼
Pipeline Parallel (PP=2)                      Pipeline Parallel (PP=2)
┌─────────────────┐                         ┌─────────────────┐
│Stage 0 │Stage 1│                         │Stage 0 │Stage 1│
└─────────────────┘                         └─────────────────┘
     │       │                                   │       │
     ▼       ▼                                   ▼       ▼
Tensor Parallel (TP=2)                       Tensor Parallel (TP=2)
┌────┬────┐ ┌────┬────┐                    ┌────┬────┐ ┌────┬────┐
│GPU0│GPU1│ │GPU2│GPU3│                    │GPU4│GPU5│ │GPU6│GPU7│
└────┴────┘ └────┴────┘                    └────┴────┘ └────┴────┘

Combination example:

• 8-GPU server
• TP=4 (4 cards for tensor parallelism per layer)
• PP=2 (2 pipeline stages)
• Total: 4 × 2 = 8 cards

Technical Challenges and Solutions

Challenge 1: Communication Overhead

Problem: Data transfer between GPUs can become a bottleneck.

Solutions:

• Use NVLink (5-10x faster than PCIe)
• Use NVSwitch (full interconnect)
• Optimize overlap of communication and computation

Challenge 2: Load Balancing

Problem: In pipeline parallelism, different stages may have unequal computation loads.

Solutions:

• Reasonable layer partitioning
• Use Interleaved Schedule

Challenge 3: KV Cache Management

Problem: KV Cache management is more complex in distributed environments.

Solutions:

• Distributed KV Cache pool
• Cross-node PagedAttention

Framework Support

Usage Examples

vLLM Distributed Inference

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## 4-card tensor parallelism
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-70b-chat-hf \
    --tensor-parallel-size 4

TensorRT-LLM Distributed Inference

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## Build engine with tensor parallelism support
trtllm-build --checkpoint_dir ./llama-70b \
             --output_dir ./engine \
             --tp_size 4 \
             --pp_size 2

## Run (using mpirun)
mpirun -n 8 python run_inference.py

Performance Considerations

Tensor Parallel vs Pipeline Parallel

How to Choose?

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Single machine multi-GPU (e.g., 8×A100-SXM4):
  → Prefer TP, fully utilize NVLink

Multi-machine deployment:
  → PP across machines, TP within machine
  → Example: 2 machines × 4 cards = PP2 × TP4

Smaller model, high request volume:
  → Consider Data Parallelism (DP)

Practical Deployment Recommendations

1. Hardware Selection:

   • Prefer machines with NVLink (e.g., DGX, HGX)
   • High-speed network for cross-machine (InfiniBand)

2. Parallelism Planning:

   • TP degree should not exceed single-machine card count
   • PP degree = total cards / TP degree

3. Monitoring Metrics:

   • GPU utilization balance
   • Communication time ratio
   • Pipeline bubble rate

Summary

Distributed inference is essential technology for running ultra-large models. Three core strategies—Tensor Parallelism, Pipeline Parallelism, Data Parallelism—each have their characteristics, and are often combined in practice.

Key points:

1. Tensor Parallel (TP): Horizontal computation split, communication-intensive, suitable for single machine
2. Pipeline Parallel (PP): Vertical model split, less communication, suitable for cross-machine
3. Data Parallel (DP): Model replication, throughput scaling
4. Hybrid Parallel: Best practice for production environments

Understand distributed inference, and you can deploy large model services of any scale.

Kernel Fusion: Making AI Computing Take Fewer “Unnecessary Trips”

In the world of deep learning, a seemingly simple operation might involve countless data transfers behind the scenes. Kernel Fusion technology is like a smart delivery driver who combines packages that would require multiple trips into one delivery, dramatically improving AI computing efficiency.

What is a Kernel?

In GPU computing, a Kernel is a computational function that runs on the GPU. Common kernels include:

• Matrix multiplication (MatMul)
• Activation functions (ReLU, GELU)
• Normalization (LayerNorm, BatchNorm)
• Element-wise operations (addition, multiplication)

When each kernel executes, it typically needs to:

1. Read data from GPU memory (VRAM)
2. Perform computation
3. Write results back to GPU memory

The Problem: Repeated Memory Access is Too Slow!

Suppose we need to compute: y = ReLU(x + bias)

Without fusion:

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Kernel 1: Add
- Read x and bias from GPU memory
- Compute x + bias
- Write result to GPU memory (temporary storage: temp)

Kernel 2: ReLU  
- Read temp from GPU memory
- Compute ReLU(temp)
- Write result to GPU memory (final result: y)

Where’s the problem?

• Intermediate result temp is written to GPU memory, then immediately read back
• Memory access is the biggest bottleneck in GPU computing
• This “round trip” is a complete waste of time!

Kernel Fusion’s Solution

After fusion:

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Fused Kernel: Add_ReLU
- Read x and bias from GPU memory
- Compute x + bias
- Compute ReLU directly in registers/shared memory
- Write final result y to GPU memory

Effect:

• Eliminated intermediate result read/write
• Memory accesses cut in half
• Significant speed improvement!

An Analogy

Without fusion: You need to mail three packages to three addresses.

• Go to post office to send first one, go home
• Go to post office again to send second one, go home
• Go to post office again to send third one

With fusion: You bring all three packages and mail them in one trip.

What you save is all that “going home and going out again” time. In the GPU world, “going to the post office” is accessing GPU memory—this time cost is very high!

Common Fusion Patterns

1. Activation Function Fusion

The most common fusion is merging activation functions into the preceding operator:

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MatMul + ReLU → MatMul_ReLU
Conv + BatchNorm + ReLU → Conv_BN_ReLU

2. Normalization Fusion

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LayerNorm = Mean + Variance + Normalize + Scale + Shift
→ Fused into one FusedLayerNorm kernel

3. Attention Mechanism Fusion

Flash Attention is a classic kernel fusion case:

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Traditional Attention:
Q × K^T → Softmax → × V
(Each step reads/writes GPU memory)

Flash Attention:
Fused into one kernel, intermediate results stay in SRAM

4. Element-wise Operation Chain Fusion

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x → Add(bias) → Multiply(scale) → Tanh → Dropout
→ Fused into one kernel

Technical Implementation

Manual Fusion

Manually implementing a fused kernel in CUDA:

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__global__ void fused_add_relu(float* x, float* bias, float* y, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        // One read, one compute, one write
        float val = x[idx] + bias[idx];
        y[idx] = val > 0 ? val : 0;  // ReLU
    }
}

Automatic Fusion

Modern deep learning compilers can automatically perform kernel fusion:

PyTorch example:

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import torch

## Define model
class MyModel(torch.nn.Module):
    def forward(self, x, bias):
        return torch.relu(x + bias)

model = MyModel()

## Use torch.compile for automatic optimization (including kernel fusion)
optimized_model = torch.compile(model)

Fusion Benefit Analysis

Using Transformer’s FFN layer as an example:

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Traditional implementation:
Linear1 → write memory → read memory → GELU → write memory → read memory → Linear2

Fused implementation:
Linear1 → GELU → Linear2 (intermediate results stay in fast storage)

Performance comparison:

Fusion Limitations

Not all operators can be fused; some conditions must be met:

1. Data Dependencies

Only serially dependent operators can be fused. If two operators are parallel, fusion might actually reduce efficiency.

2. Compute-to-Memory Ratio

Fusion benefits depend on saved memory access time vs. added complexity:

• If computation is heavy and memory access is light, fusion benefits are minimal
• If memory access is heavy and computation is light (like element-wise ops), fusion benefits are significant

3. Shared Memory Limits

Fused kernels might need more shared memory and registers, potentially exceeding GPU limits.

Practical Application Cases

Flash Attention

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## Traditional attention - multiple separate kernels
attn_weights = torch.matmul(Q, K.transpose(-2, -1))
attn_weights = torch.softmax(attn_weights / math.sqrt(d_k), dim=-1)
output = torch.matmul(attn_weights, V)

## Flash Attention - fused kernel
from flash_attn import flash_attn_func
output = flash_attn_func(Q, K, V)  # One fused kernel

Effect: 2-4x speed improvement, 5-20x memory reduction.

TensorRT Automatic Fusion

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import tensorrt as trt

## TensorRT automatically identifies and fuses:
## Conv + BatchNorm + ReLU → CBR fused kernel
## MatMul + Add + GELU → Fused kernel

How to Determine if Fusion is Needed?

Use performance analysis tools:

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## View kernel call patterns
nsys profile python my_model.py

## If you see many small kernels called consecutively, consider fusion
## Kernel: add_kernel (0.1ms)
## Kernel: relu_kernel (0.1ms)  
## Kernel: mul_kernel (0.1ms)
## → Can be fused into one kernel

Summary

Kernel fusion is one of the core technologies in deep learning performance optimization. Its essence is reducing memory access by combining multiple small operations into one large operation, avoiding repeated read/write of intermediate results.

Key points:

1. Memory access is the bottleneck: Computing is fast, reading/writing is slow
2. Fusion reduces round trips: Data stays in fast storage for processing
3. Automatic fusion tools: TensorRT, torch.compile, etc.
4. Classic example: Flash Attention

Understand kernel fusion, and you understand one of the core secrets of GPU acceleration.

CUDA Performance Optimization: The Secret to Making GPUs Fly

If large models are a supercar, then CUDA optimization is the tuning technique that unleashes the car’s ultimate performance. Today, we’ll reveal how to make GPUs run faster in an easy-to-understand way.

What is CUDA?

CUDA (Compute Unified Device Architecture) is NVIDIA’s parallel computing platform and programming model. Simply put, it’s the “language” and “toolbox” that allows us to write programs on GPUs.

Why are GPUs so important?

Computing 1+1=? A professor answers instantly. But computing a hundred million addition problems? Ten thousand students working simultaneously is much faster than one professor!

Matrix operations in deep learning are exactly this type of “massive simple computations” scenario, which is why GPUs became the workhorse of AI.

Why Do We Need CUDA Optimization?

“Isn’t having lots of GPU cores enough?”—Not really.

An unoptimized GPU program is like:

• Hiring ten thousand employees, but only 1000 are working while others are slacking
• Buying an 8-lane highway, but everyone’s crammed into one lane
• Hiring a top chef, but ingredient supply can’t keep up

The goal of CUDA optimization: Keep every GPU core busy, and let data flow without blockages.

Core Concepts of CUDA Optimization

1. Understanding GPU Architecture

GPUs have a multi-layered structure:

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GPU
├── SM (Streaming Multiprocessor) × N  ← Multiple streaming processors
│   ├── CUDA Core × M                   ← Compute cores
│   ├── Shared Memory                   ← Shared memory (very fast)
│   ├── L1 Cache                        ← Level 1 cache
│   └── Registers                       ← Registers (fastest)
├── L2 Cache                            ← Level 2 cache
└── Global Memory (HBM)                 ← Video memory (large but slow)

Key insight: The closer data is to compute cores, the faster the access. The core of optimization is keeping data in “fast” places as much as possible.

2. Memory Hierarchy and Access Speed

Analogy:

• Registers = Pen in your hand (instant)
• Shared Memory = Pencil case on your desk (reach over)
• L2 Cache = Bookshelf behind you (turn around)
• Global Memory = Library warehouse (need to walk there)

Core Optimization Techniques

1. Memory Coalescing

GPU reads memory in “chunks” (128 bytes). If multiple threads access data that happens to be in one chunk, it can all be read at once.

Bad example (inefficient):

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Thread 0 accesses address 0
Thread 1 accesses address 1000
Thread 2 accesses address 2000
→ Requires 3 memory accesses

Good example (efficient):

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Thread 0 accesses address 0
Thread 1 accesses address 4
Thread 2 accesses address 8
→ 1 memory access handles all

Optimization method: Have adjacent threads access adjacent memory addresses.

2. Using Shared Memory

For data that needs to be accessed multiple times, first load from global memory to shared memory, then reuse:

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__global__ void matmul_optimized(float* A, float* B, float* C) {
    __shared__ float tile_A[TILE_SIZE][TILE_SIZE];
    __shared__ float tile_B[TILE_SIZE][TILE_SIZE];
    
    // 1. Load data blocks to shared memory
    tile_A[ty][tx] = A[row * N + tx];
    tile_B[ty][tx] = B[ty * N + col];
    __syncthreads();  // Wait for all threads to finish loading
    
    // 2. Compute in shared memory (fast!)
    for (int k = 0; k < TILE_SIZE; k++) {
        sum += tile_A[ty][k] * tile_B[k][tx];
    }
}

Effect: Reduces accesses to slow global memory.

3. Avoiding Bank Conflicts

Shared memory is divided into 32 banks. If multiple threads simultaneously access different addresses in the same bank, conflicts occur and execution becomes serial.

Analogy: 32 people going to 32 different ATMs simultaneously = no conflict. 32 people fighting for 1 ATM = waiting in line forever.

Solution: Design data layout carefully so thread accesses are distributed across different banks.

4. Optimizing Thread Configuration

Threads are organized in three levels:

• Thread: Smallest execution unit
• Block: A group of threads sharing shared memory
• Grid: Collection of all thread blocks

Optimization points:

• Threads per block should be multiples of 32 (Warp size)
• Usually choose 128 or 256 threads per block
• Ensure enough blocks so all GPU SMs have work to do

5. Instruction-level Optimization

Use fast math functions:

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// Slow
float result = sin(x);

// Fast (slightly less precise but sufficient)
float result = __sinf(x);

Leverage Tensor Cores:
Modern NVIDIA GPUs have dedicated Tensor Cores for matrix operations, much faster than regular CUDA Cores:

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// Matrix multiplication using Tensor Core
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);

Practical: Matrix Multiplication Optimization Comparison

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Optimization Stage       | Performance (relative)
------------------------|----------------------
Naive implementation    | 1x
+ Memory coalescing     | 3x
+ Shared memory tiling  | 10x
+ Avoid bank conflict   | 12x
+ Tensor Core           | 50x+

Performance Analysis Tools

Optimization requires measurement. NVIDIA provides powerful analysis tools:

Usage example:

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## Profile overall program performance
nsys profile ./my_cuda_program

## Deep analysis of a specific kernel
ncu --set full ./my_cuda_program

Common Optimization Checklist

Application in AI Frameworks

Frameworks like PyTorch and TensorFlow extensively use optimized CUDA kernels under the hood:

• cuBLAS: Matrix operation library
• cuDNN: Deep learning primitives library
• Flash Attention: Optimized attention mechanism implementation

When you call torch.matmul(), countless engineer-optimized CUDA code is working behind the scenes.

Summary

CUDA performance optimization is the key technology to unleash GPU’s true power. Core ideas include:

1. Reduce memory access: Memory is the bottleneck, reuse data as much as possible
2. Utilize memory hierarchy: Put hot data in faster storage layers
3. Maintain parallelism: Keep all compute units busy
4. Use specialized hardware: Tensor Cores and other acceleration units

Master these techniques, and you’ll master the magic of making AI models “fly.”

Continuous Batching: The Black Magic That Keeps Large Model Services “Never Idle”

When you chat with ChatGPT, the servers behind it might be serving thousands of users simultaneously. How do we efficiently handle these concurrent requests? Continuous Batching technology emerged to keep GPUs running at full capacity, dramatically improving the throughput of large model services.

First, Understanding the Problem with Traditional Batching

What is Batching?

In deep learning, batching means packaging multiple requests together for simultaneous processing, rather than processing them one by one. This better utilizes the GPU’s parallel computing capabilities.

Analogy: It’s like cooking at a restaurant—stir-frying one dish at a time is inefficient, but doing four at once (if the wok is big enough) is more efficient.

The Dilemma of Traditional Static Batching

In traditional Static Batching:

1. The server waits to collect a batch of requests (say, 8)
2. These 8 requests are sent to the model together
3. It waits until all requests complete before processing the next batch

Here’s the problem:

Suppose among these 8 requests:

• User A asks: “What’s 1+1?” → Completes after generating 3 tokens
• User B asks: “Write me a 1000-word article” → Needs to generate 500 tokens

Result with Static Batching:

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Timeline:
[Token 1] A✓ B○ C○ D○ E○ F○ G○ H○
[Token 2] A✓ B○ C✓ D○ E○ F○ G○ H○
[Token 3] A✓ B○ C✓ D✓ E✓ F○ G○ H○  ← A, C, D, E completed
...
[Token 500] waiting... B○ waiting... waiting...  ← Everyone waiting for B

User A should have received their response long ago, but has to wait for User B’s long article to finish! During this time:

• GPU resources wasted: Completed requests still occupy slots
• Increased latency: Short requests are “held back” by long ones
• Poor user experience: Questions that could be answered instantly take forever

How Continuous Batching Solves the Problem

Continuous Batching (also called Iteration-level Batching or In-Flight Batching) has a core idea:

Reschedule the batch at each iteration step (every token generated)

How It Works

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Initial state: Processing [A, B, C, D]

After Token 1:
- A completed ✓ → Remove from batch, immediately return result to User A
- B, C, D continue
- New request E joins → Batch becomes [B, C, D, E]

After Token 2:
- C completed ✓ → Remove, return
- New requests F, G join → Batch becomes [B, D, E, F, G]

...and so on

Effects:

• Completed requests immediately release resources
• New requests join anytime to the running batch
• GPU always runs at full load, no idle time

A Visual Analogy

Static Batching is like a traditional bus:

• Must wait for everyone to board/disembark at each stop
• Departs when full, everyone gets off together at the terminal
• Want to get off midway? Sorry, wait until the end

Continuous Batching is like a “cloud rail” or “robo-taxi”:

• Passengers can get on and off anytime
• Get off immediately upon reaching destination, seat is freed immediately
• New passengers can immediately take empty seats
• Vehicle always operates efficiently

Key Technical Implementation Points

1. Sequence-level Scheduling

Traditional batching schedules by “batch”; Continuous Batching schedules by “sequence”:

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class ContinuousBatcher:
    def __init__(self, max_batch_size=32):
        self.running_sequences = []  # Sequences currently generating
        self.waiting_queue = []       # Waiting queue
        self.max_batch_size = max_batch_size
    
    def step(self):
        # 1. Remove completed sequences
        completed = [s for s in self.running_sequences if s.is_done()]
        for seq in completed:
            self.running_sequences.remove(seq)
            self.return_result(seq)
        
        # 2. Fill empty slots
        while len(self.running_sequences) < self.max_batch_size:
            if self.waiting_queue:
                new_seq = self.waiting_queue.pop(0)
                self.running_sequences.append(new_seq)
            else:
                break
        
        # 3. Execute one inference step
        self.forward_one_step(self.running_sequences)

2. Dynamic Memory Management

Since batch composition changes constantly, memory management becomes complex:

• Requires technologies like PagedAttention
• KV Cache allocated and freed on demand
• Supports sequences of different lengths coexisting

3. Prefix Sharing Optimization

When multiple requests share the same prefix (like the same System Prompt):

• Can share prefix KV Cache
• New requests joining directly reuse it

Advantages of Continuous Batching

Performance Comparison

Using LLaMA-7B model as an example:

Mainstream Framework Support

The following inference frameworks implement Continuous Batching:

Usage Example (vLLM)

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from vllm import LLM, SamplingParams

## Initialize model (automatically enables Continuous Batching)
llm = LLM(model="meta-llama/Llama-2-7b-chat-hf")

## Batch requests - automatically scheduled, short ones return first
prompts = [
    "What is 1+1?",           # Short request
    "Write a 500-word essay about AI...",  # Long request
    "Hello!",                  # Short request
]

outputs = llm.generate(prompts, SamplingParams(max_tokens=1024))

## Note: Although the code looks like batch processing,
## internally it uses Continuous Batching for optimized scheduling

Challenges and Solutions

Challenge 1: Increased Scheduling Complexity

• Problem: Each step must determine which are complete, which continue
• Solution: Efficient data structures and scheduling algorithms

Challenge 2: Memory Fragmentation

• Problem: Varying sequence lengths cause scattered memory allocation
• Solution: PagedAttention solves fragmentation issues

Challenge 3: Request Starvation

• Problem: Long requests might keep getting “cut in line” by short ones
• Solution: Priority scheduling, fair scheduling policies

Summary

Continuous Batching is one of the core technologies in modern LLM inference services. It breaks the traditional batching limitation of “wait until all are ready,” achieving dynamic scheduling at the request level. Combined with memory optimization techniques like PagedAttention, Continuous Batching increases large model service throughput by several times while reducing user wait times.

If you’re deploying LLM services, choosing an inference framework that supports Continuous Batching (like vLLM, TensorRT-LLM, SGLang) would be a wise choice.

PagedAttention: Freeing Large Model Inference from “Memory Anxiety”

In the world of Large Language Model (LLM) inference, memory management has always been a headache for engineers. Today’s topic, PagedAttention technology, cleverly solves the memory challenges in LLM inference by borrowing classic ideas from operating systems. This technology is the core innovation of the vLLM framework. Let’s dive deep into understanding it.

Starting from a Pain Point

Imagine you’re running an AI chat service with 100 users simultaneously talking to the AI. Each user’s conversation varies in length—some only ask “What’s the weather today?”, while others write a 2000-word story asking the AI to continue it.

Problems with Traditional Approaches:

Traditional LLM inference requires pre-allocating a fixed-size memory block for each user (for storing KV Cache). To accommodate the “novel writer” user, you have to allocate space for 2000 words to everyone.

The result?

• The user who only asked about weather has 99% of their allocated memory wasted
• GPU memory quickly fills up with “air”
• Instead of serving 100 users, you can only serve 20

This is the problem PagedAttention solves.

What is KV Cache?

Before understanding PagedAttention, let’s clarify what KV Cache is.

When an LLM generates text, it outputs one token at a time (autoregressive generation). For each new token generated, the model needs to “look back” at all previously generated content.

Computing from scratch each time is too inefficient. So we store the Key and Value vectors from the attention mechanism that were previously computed—this is the KV Cache.

Analogy: It’s like writing a long article where you have to read from the first sentence every time you write a new one—too exhausting. KV Cache is like your “memory notes,” recording key information from before, so you only need to check your notes when writing new sentences.

Core Ideas of PagedAttention

PagedAttention’s inspiration comes from virtual memory paging technology in operating systems.

How Do Operating Systems Do It?

In modern operating systems, memory needed by programs isn’t allocated all at once, but divided into small “pages” allocated on demand:

• Program says: “I might need 8GB of memory”
• OS says: “Okay, but you’re only using 100MB now, I’ll give you that first”
• When the program actually needs more, new pages are allocated

PagedAttention does the same thing for KV Cache:

1. Paged Storage: Splits continuous KV Cache into fixed-size “blocks”
2. On-demand Allocation: New blocks are allocated only when generating new tokens
3. Non-contiguous Storage: Blocks don’t need to be contiguous in physical memory; a “page table” manages the mapping

Visualizing PagedAttention

Traditional Approach:

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User A: [████████████____________________] ← Large pre-allocation, lots of waste
User B: [████________________________] ← Same waste
User C: [██████████████████______________] ← Still wasted

PagedAttention:

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Block Pool: [A1][B1][A2][C1][B2][C2][A3][C3][free][free][free]...

User A → Page Table: [A1, A2, A3]     ← Only used 3 blocks
User B → Page Table: [B1, B2]         ← Only used 2 blocks  
User C → Page Table: [C1, C2, C3]     ← Only used 3 blocks

Effect: No waste—each user uses exactly the memory they need.

Technical Details

1. Block

• Each Block contains KV vectors for a fixed number of tokens
• Typical Block size: 16 tokens
• Block is the minimum unit of memory allocation

2. Block Table (Page Table)

• Each request maintains a Block Table
• Records which physical Blocks the request’s KV Cache uses
• Similar to OS page tables, implementing virtual-to-physical mapping

3. On-demand Allocation

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Timeline:
T1: User inputs "Hello" → Allocate Block #1
T2: Model generates "," → Block #1 still has space, continue using
T3: Model generates "I am..." → Block #1 is full, allocate Block #2
...

4. Memory Release

When a request completes or is interrupted, its occupied Blocks are immediately returned to the free pool for other requests.

Advantages of PagedAttention

1. Near-zero Waste

Research shows traditional methods have memory waste rates of 60-80%. PagedAttention reduces waste to nearly 0% (only the last Block might have some unused space).

2. Higher Concurrency

Same GPU memory can handle 2-4x more concurrent requests.

3. Longer Context Support

With improved memory utilization, the same hardware can support longer conversation contexts.

4. Flexible Memory Sharing

PagedAttention also supports an advanced feature: Copy-on-Write

When multiple requests share the same prefix (like the same system prompt):

• They can share the same Blocks
• New Blocks are copied only when modification is needed
• Further memory savings

Conceptual Code Example

Although actual implementation is complex, the core idea can be understood like this:

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class PagedKVCache:
    def __init__(self, block_size=16, num_blocks=1000):
        self.block_size = block_size
        # Pre-allocate Block pool
        self.block_pool = [Block() for _ in range(num_blocks)]
        self.free_blocks = list(range(num_blocks))
    
    def allocate_block(self):
        """Allocate a new Block on demand"""
        if self.free_blocks:
            return self.free_blocks.pop()
        raise MemoryError("No free blocks!")
    
    def free_block(self, block_id):
        """Release Block back to pool"""
        self.free_blocks.append(block_id)

class Request:
    def __init__(self, cache):
        self.cache = cache
        self.block_table = []  # Page table
        self.num_tokens = 0
    
    def add_token(self, kv_data):
        # Check if current Block is full
        if self.num_tokens % self.cache.block_size == 0:
            # Need new Block
            new_block = self.cache.allocate_block()
            self.block_table.append(new_block)
        
        # Write KV data
        current_block = self.block_table[-1]
        # ... write logic
        self.num_tokens += 1

Application in vLLM

vLLM is the first inference framework to implement PagedAttention:

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## Install vLLM
pip install vllm

## Start server (automatically uses PagedAttention)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf

Users don’t need any extra configuration—vLLM automatically applies PagedAttention optimization.

Performance Comparison

Summary

PagedAttention is an important innovation in the LLM inference field. By borrowing the paged memory management concept from operating systems, it transforms KV Cache memory management from “static pre-allocation” to “dynamic on-demand allocation,” dramatically improving memory utilization and system throughput.

This technology teaches us that sometimes the best solution to new problems lies hidden in the wisdom of classic techniques.

SGLang: The Inference Powerhouse for “Structured Output” from Large Models

When working with Large Language Models (LLMs), have you ever encountered this frustration: you only want the model to output data in JSON format, but it goes off on a tangent with “creative freedom”? SGLang is a next-generation LLM inference framework designed precisely to solve such problems. Let’s get to know this innovative tool from UC Berkeley.

What is SGLang?

SGLang (Structured Generation Language) is a high-performance LLM inference engine and programming language developed by the UC Berkeley team. Its core goal is: to make LLM output more controllable and structured while maintaining extremely high inference performance.

In one sentence: SGLang = High-performance inference + Structured output control

Why Do We Need SGLang?

Imagine this scenario:

You ask ChatGPT to extract key information from an article and return it in JSON format:

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Please extract the following information and return in JSON format:
- Author name
- Publication date
- Main points

The model might return:

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Okay, let me help you extract the information:

{
  "author": "John Smith",
  "date": "January 1, 2024",
  "main_points": ["Point 1", "Point 2"]
}

Hope this helps! Let me know if you need more information.

The problem—the model added “fluff” before and after the JSON, and your program crashes when trying to parse the JSON.

SGLang’s Solution: Through constrained generation, force the model to output only content that matches the expected format.

Core Features of SGLang

1. Structured Generation (Constrained Decoding)

SGLang’s most powerful feature is constrained decoding. You can precisely control the model’s output format:

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from sglang import gen, select

## Force model to choose from fixed options
answer = select("Yes", "No", "Uncertain")

## Force output matching a regex pattern
phone = gen(regex=r"\d{3}-\d{4}-\d{4}")

## Force output of valid JSON
data = gen(json_schema={
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    }
})

Analogy: This is like giving students a multiple-choice test instead of asking them to write an essay. Answers must be chosen from A, B, C, D—no “creative freedom” allowed.

2. RadixAttention: Smart Cache Reuse

SGLang introduces the innovative RadixAttention mechanism:

• Uses a Radix Tree to manage KV Cache
• Automatically identifies and reuses caches with the same prefix
• Multiple requests share common parts, dramatically improving efficiency

Example:

Suppose 100 users are all using the same system prompt. Traditional methods need to compute it 100 times. RadixAttention computes it once and reuses it for all users.

3. Frontend Programming Language

SGLang provides an intuitive programming approach to orchestrate complex LLM calls:

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@sgl.function
def multi_turn_qa(s, question1, question2):
    s += sgl.system("You are a helpful assistant.")
    s += sgl.user(question1)
    s += sgl.assistant(sgl.gen("answer1", max_tokens=100))
    s += sgl.user(question2)
    s += sgl.assistant(sgl.gen("answer2", max_tokens=100))

This approach is cleaner and more maintainable than traditional string concatenation.

4. High-Performance Inference Backend

SGLang’s inference is very fast, thanks to:

• Continuous Batching
• Optimized CUDA Kernels
• Efficient memory management
• Speculative Decoding support

SGLang vs Other Frameworks

Practical Application Scenarios

Scenario 1: API Data Extraction

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@sgl.function
def extract_info(s, text):
    s += f"Extract structured information from the following text:\n{text}"
    s += sgl.gen("result", json_schema={
        "type": "object",
        "properties": {
            "entities": {"type": "array"},
            "sentiment": {"enum": ["positive", "negative", "neutral"]},
            "summary": {"type": "string"}
        }
    })

Scenario 2: Multiple Choice Q&A

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@sgl.function  
def multiple_choice(s, question, options):
    s += f"Question: {question}\nOptions: {options}\nPlease select the correct answer:"
    s += sgl.select(["A", "B", "C", "D"], name="answer")

Scenario 3: Code Generation

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@sgl.function
def generate_function(s, description):
    s += f"Generate a Python function based on the following description:\n{description}"
    s += sgl.gen("code", regex=r"def \w+\([^)]*\):[\s\S]+")

Getting Started

Installation

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pip install sglang[all]

Launch Server

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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000

Usage Example

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import sglang as sgl

@sgl.function
def simple_qa(s, question):
    s += sgl.user(question)
    s += sgl.assistant(sgl.gen("answer", max_tokens=256))

## Run
state = simple_qa.run(question="What is machine learning?")
print(state["answer"])

Performance

According to official benchmarks, SGLang performs excellently in various scenarios:

• JSON Mode Generation: 3-5x faster than other frameworks
• Shared Prefix Scenarios: RadixAttention provides 2-4x speedup
• Multi-turn Dialogue: Cache reuse significantly reduces latency

Use Cases

SGLang is particularly suitable for:

• ✅ API services requiring structured JSON output
• ✅ Constraining models to choose from fixed options
• ✅ Multiple requests sharing the same prefix (like system prompts)
• ✅ Complex multi-turn dialogues and reasoning chains
• ✅ High-performance production deployments

Summary

SGLang is an LLM inference framework that perfectly combines structured output control with high-performance inference. Through constrained decoding technology, it makes LLM output controllable and predictable; through optimizations like RadixAttention, it achieves extremely high inference efficiency while maintaining flexibility. If your application requires reliable structured output, SGLang is definitely worth trying.

TensorRT-LLM: NVIDIA’s Inference Acceleration Engine Built for Large Language Models

As Large Language Models (LLMs) sweep across the globe, how to make these “giants” run faster and cheaper has become one of the industry’s top concerns. NVIDIA’s TensorRT-LLM is a powerful tool designed precisely to solve this problem. Today, let’s understand this tool in an easy-to-understand way.

What is TensorRT-LLM?

TensorRT-LLM is an open-source library designed by NVIDIA specifically for LLM inference optimization. You can think of it as the “LLM-specialized version” of TensorRT (NVIDIA’s general deep learning inference optimizer).

An analogy:

• TensorRT is like a high-performance “multi-purpose SUV” that can handle any road.
• TensorRT-LLM is a “supercar” specifically designed for the “highway” (LLM inference scenarios)—on this track, it runs faster than anything else.

Why Do We Need TensorRT-LLM?

Large Language Models (such as GPT, LLaMA, Mistral, etc.) have several notable characteristics:

1. Massive Parameters: Often billions, hundreds of billions, or even trillions of parameters
2. Autoregressive Generation: Outputs tokens one by one, requiring complete computation for each generated token
3. High Memory Usage: Storing model parameters and intermediate states requires significant GPU memory
4. Latency Sensitive: Users want conversational responses as fast as possible

Ordinary inference methods simply cannot “feed” these large models efficiently, while TensorRT-LLM uses a series of advanced technologies to make LLM inference faster and more efficient.

Core Technologies of TensorRT-LLM

1. In-Flight Batching (Dynamic Batching)

Traditional batching waits until a batch of requests is ready before processing them together. But user queries vary in length—some are three words, others are three hundred—waiting for them all is too inefficient.

TensorRT-LLM uses In-Flight Batching:

• New requests can “jump in” and join an ongoing batch at any time
• Completed requests immediately release resources
• GPU utilization improves dramatically

Analogy: It’s like a restaurant that doesn’t wait for everyone at a table to finish ordering before sending orders to the kitchen—whoever orders first gets served first, keeping the kitchen always busy.

2. Paged KV Cache

During LLM inference, large amounts of Key-Value cache need to be stored (to remember previously generated content). Traditional methods require pre-allocating fixed-size memory, which easily leads to waste.

TensorRT-LLM borrows the virtual memory paging concept from operating systems:

• KV Cache is allocated on demand—use only what you need
• Avoids memory fragmentation
• Supports longer contexts and more concurrent requests

3. Multiple Quantization Techniques

TensorRT-LLM supports various quantization schemes to compress models:

• FP8 Quantization: Best performance on Hopper architecture GPUs
• INT8/INT4 Quantization: Significantly reduces memory usage
• AWQ, GPTQ, SmoothQuant: Various advanced quantization algorithms

Quantization is like compressing a “high-resolution image” into a “thumbnail”—you lose some detail, but storage space and transfer speed improve dramatically.

4. Tensor Parallelism and Pipeline Parallelism

When a single GPU cannot fit the entire model, TensorRT-LLM supports:

• Tensor Parallelism: “Horizontally slices” a computation task across multiple GPUs
• Pipeline Parallelism: “Vertically slices” the model into multiple stages, processing like an assembly line

This allows ultra-large models to run efficiently.

5. Optimized Attention Mechanisms

TensorRT-LLM integrates multiple efficient attention implementations:

• Flash Attention: Reduces memory access, accelerates computation
• Multi-Query Attention (MQA) and Grouped-Query Attention (GQA): Reduces KV Cache usage

TensorRT-LLM vs Other Inference Frameworks

Use Cases

TensorRT-LLM is particularly suitable for the following scenarios:

• Large-scale Online Services: ChatBots that need to handle many concurrent requests
• Low-latency Applications: Real-time conversation systems with strict response time requirements
• Cost-sensitive Scenarios: Serving more users with fewer GPUs
• Private Deployment: Deploying LLM services within enterprises

Getting Started

The basic workflow for using TensorRT-LLM:

1. Installation: Install via pip or Docker
2. Convert Model: Convert HuggingFace models to TensorRT-LLM format
3. Build Engine: Compile to generate optimized inference engine
4. Deploy Service: Use Triton Inference Server or custom service

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## Example: Converting LLaMA model
python convert_checkpoint.py --model_dir ./llama-7b \
                             --output_dir ./trt_ckpt \
                             --dtype float16

## Build engine
trtllm-build --checkpoint_dir ./trt_ckpt \
             --output_dir ./trt_engine \
             --gemm_plugin float16

Performance

According to NVIDIA’s official data, TensorRT-LLM can achieve compared to native implementations:

• Throughput Improvement: 2-5x or even higher
• Latency Reduction: Both time-to-first-token and generation latency significantly reduced
• Memory Efficiency: Supports longer contexts and more concurrency

Summary

TensorRT-LLM is NVIDIA’s “dedicated supercar” built for LLM inference. Through dynamic batching, paged KV cache, various quantization techniques, and hardware-level optimizations, it makes LLM inference faster, more efficient, and more powerful. If you’re deploying LLM services, TensorRT-LLM is definitely worth trying.

Unveiling the “Turbo Engine” of AI Art: UniPC AYS Sampling Method

In the world of Artificial Intelligence today, AI Art generation (like Stable Diffusion) has become ubiquitous. You type in a sentence, and the AI conjures up a beautiful image. But have you ever wondered how AI transforms a field of blurry static into a masterpiece? In this process, there is a key player called the “Sampler.”

Today, we are introducing a rising star in the sampler family: UniPC AYS.

1. The Basics: How Does AI Art Work?

To understand UniPC AYS, we first need an analogy for the AI painting process.

Imagine sculpting a block of stone

Or even simpler, imagine wiping fog off a window.

• Initial State (Gaussian Noise): When the AI starts, it’s like facing a window covered in thick fog (static pixels). You can’t see anything clearly.
• Generation Process (Denoising): The AI receives your command (e.g., “a cat”). It starts wiping away the fog step by step, removing the random dots that don’t belong to a “cat,” slowly revealing the outline, fur, and eyes.
• Sampling Steps: The number of times you wipe the fog. More wipes (more steps) usually mean a more detailed image, but it also takes longer.

The Sampler is the strategist who creates the plan for “how to wipe.” Some samplers are slow but meticulous; others are fast but might be rough.

2. What is UniPC? (The Unified Predictor)

UniPC (Unified Predictor-Corrector) is a powerful sampling algorithm developed in recent years.

Analogy: The Experienced Master Painter

If you ask a novice to paint, they might stop after every brushstroke to check the model, afraid of making a mistake. It’s slow.
UniPC, however, is like an old master painter. He looks at the model once and can accurately predict how the next several brushstrokes should flow (Predictor). After painting, he takes a quick glance to make minor adjustments (Corrector).

• Core Advantage: UniPC is extremely efficient. Not only does it paint fast, but it can produce very high-quality images in very few steps (e.g., only 10 steps), whereas traditional methods might need 20 to 50 steps.

3. What is AYS? (The Adaptive Schedule)

AYS stands for Adaptive Yield Sampling. It is an optimization scheduling strategy related to recent research often associated with NVIDIA.

Analogy: The Smart Travel Planner

Suppose you want to travel from the starting point (pure noise) to the destination (a perfect picture).

• Standard Schedule: This is like a bus that stops at every single station. It doesn’t matter if the road is straight or curved, or if the scenery is important; it moves at a constant speed and stops at fixed intervals. It’s stable, but it wastes time.
• AYS Schedule (Adaptive): This is like a smart private driver.
  • When the road conditions are complex (when the image is still blurry and the foundational structure is being formed), it drives slower and stops more often to process details.
  • When the road is simple (when the image is basically formed and just needs slight polishing), it steps on the gas and skips unnecessary stops.

The core idea of AYS is: Spend precious computing resources (steps) on the moments that matter most for image quality.

4. The Power Combo: UniPC AYS

When you combine UniPC (the master painter with intuitive foresight) with AYS (the smart travel planner), magic happens.

UniPC AYS = Extreme Speed + High Quality

Selecting UniPC AYS as your sampler in software like Stable Diffusion WebUI or ComfyUI means:

1. Blazing Speed: It often produces a usable, detailed image in 10 steps or even fewer.
2. High Efficiency: It uses AYS to optimize the “denoising strength” of each step, combined with UniPC’s powerful predictive capabilities, completing the most complex painting tasks with the minimum number of actions.

What does this mean for the user?

• Save Time: Generating a good image used to take 5-10 seconds; now it might only take 2-3 seconds.
• Save GPU Power: Generating the same image requires less calculation, so your graphics card doesn’t have to work as hard.
• No More Guesswork: You no longer need to crank the steps up to 50 or 100 for quality. UniPC AYS tells you: Less is more.

Summary Chart

For a more intuitive understanding, let’s look at this comparison table:

Conclusion

Technology is always evolving towards being “faster, better, and cheaper.” UniPC AYS is a prime example of this trend. It proves that AI isn’t just about piling up raw computing power, but about learning how to work “smartly.” Next time you are using AI art software, try switching the sampler to UniPC combined with the AYS schedule, and experience that “flying” sensation for yourself

AI Art’s Unsung Hero: Explaining the UniPC Sampling Method

In the magical world of AI-generated imagery (AI Art), we type a few words, and seconds later, a masterpiece appears. But did you know there is a crucial step in this process called “Sampling”? Today, our protagonist is a highly efficient sampling method that has garnered much attention recently—UniPC.

1. What is “Sampling”?

Before diving into UniPC, we need to understand how AI paints. The most popular technology currently used is called the “Diffusion Model.”

The Analogy: From Static Noise to a Clear Photo

Imagine you have a clear photo, and you slowly sprinkle sand (noise) over it. The more sand you add, the blurrier the photo becomes, until finally, it’s just a completely random pile of sand (in AI terms, this is called “Gaussian Noise”).

AI’s painting process is essentially the reverse operation.

1. The AI faces a pile of meaningless random noise (like the static on an old TV).
2. It starts to “sweep away the sand” step by step.
3. With each sweep, it guesses: “What should the pattern underneath look like?”
4. After dozens or even hundreds of steps of cleaning and correcting, a clear image is revealed.

This process of “removing noise step by step to gradually restore the image” is Sampling. The Sampler is the “cleaner” responsible for executing this work.

2. Why Do We Need UniPC?

Traditional sampling methods work, but they have a big problem: they are slow.

To create a perfect image, the “cleaner” might need to sweep 50 or even 100 times to ensure every detail is correct. This means generating one image can take a long time. We always want AI to paint fast and well.

UniPC appeared to solve the speed problem.

3. The Concept: Predictor-Corrector

UniPC stands for Unified Predictor-Corrector. It sounds complex, but its core principle can be understood this way:

The Analogy: A Veteran Driver on a Curved Road

Imagine you are driving a car (this is the image generation path), and the road ahead is winding (the process of the image going from blurry to clear is non-linear).

• Ordinary Sampler (Novice Driver):
  Stops every few feet to check the map carefully and calculate the next move. Very cautious, therefore very slow.

• UniPC (Veteran Driver):
  It possesses a powerful “anticipation” ability. With one look at the road, it can Predict the curvature of the turn and steer through it confidently.
  However, blindly rushing could lead to a crash (ruined image), so it also has a Correct mechanism. As soon as it senses a slight deviation between the actual road and its prediction, it immediately fine-tunes the steering wheel to pull the car back on track.

UniPC’s Two Killer Features:

1. Unified: It is compatible with various types of diffusion models, whether it’s for anime style or photorealistic photos, it can handle them all.
2. Few Steps: Because it predicts accurately and corrects quickly, while others might need 50 steps to finish a drawing, UniPC might only need 10 steps!

4. What is “Trailing”?

In many AI software interfaces (like Stable Diffusion WebUI), you might not see the word Trailing directly after UniPC. However, in academic papers or specific implementations, strategies regarding how UniPC handles time steps are discussed.

Although “Trailing” isn’t part of the UniPC name itself, it relates to how UniPC handles mathematical sequences. If we view the generation process as a timeline: t1, t2, t3....

Trailing here can be understood as using past experience to assist current decisions.

Analogy: Driving Using the Rearview Mirror

When UniPC predicts the next move, it doesn’t just look at the current position; it also references several points it has already passed (Previous Time Steps). It’s like a driver who, if unsure how sharp the turn ahead is, recalls the curvature trend of the road segment they just drove through.

By analyzing the “past data trajectory/trailing points,” UniPC can draw a smoother, more precise curve, thereby reaching the destination (generating a perfect image) in very few steps.

5. Summary: What Does This Mean for You?

If you are an AI art user, choosing the UniPC sampler means:

1. Blazing Speed: The time to generate images is drastically reduced. While others generate one image, you can generate two.
2. Uncompromised Quality: Despite the speed, the details and clarity of the image remain top-tier.
3. Computational Efficiency: For computers with lower-end graphics cards, UniPC is a very friendly choice because it achieves the same result with less computational power.

In short, UniPC is like installing a turbocharger on the AI painting engine, making creation both effortless and efficient.

The Forgotten Time Traveler: Demystifying DDIM Trailing

In the wondrous world of AI art, we type in text, and the AI conjures up a stunning image. This magic relies on a technology known as “Diffusion Models.” Within this technology, the strategy for restoring a clear image from a mess of noise is called a “Sampling Method.”

Today, we are going to demystify a specific and somewhat niche concept: DDIM Trailing.

Don’t worry about complex formulas; we will use the analogy of “Restoring an Ancient Painting” to explain it.

1. The Basic Concept: Diffusion Models are like “Spilling Ink and Restoring”

Imagine you have an exquisite oil painting (like the Mona Lisa).

1. Adding Noise (Spilling Sand): If we sprinkle a little sand on the painting, it becomes blurred. Sprinkle more, and it gets blurrier. Repeat this a thousand times, and the painting eventually becomes a meaningless pile of gravel (pure noise). This process is called “Diffusion.”
2. Denoising (Restoring): What the AI learns is how to “reverse” this process. It looks at the pile of gravel, tries to guess how the sand was distributed one step before, and removes the sand bit by bit until the Mona Lisa reappears.

This process of “removing sand bit by bit” is Sampling.

2. What is DDIM? (The Fast Lane)

The original restoration method (DDPM) is very slow, like an obsessive craftsman who must strictly follow the reverse steps of spilling the sand, cleaning up step-by-step. It might take 1,000 steps.

DDIM (Denoising Diffusion Implicit Models) is like an experienced master. It realizes that you don’t actually need to take every single step. It can “skip steps.” For example, by looking at the current sand distribution, it can predict what it will look like 10 steps later, or even roughly guess the original painting immediately, thus greatly speeding up the process. A task that took 1,000 steps can now be done in just 50.

3. The Protagonist: DDIM Trailing

So, what is Trailing?

This involves a very subtle alignment issue with Timesteps in the underlying code.

Metaphor: The Hopscotch Countdown

Imagine playing a “Time Travel” hopscotch game.

• Start: Square #1000 (Full of sand).
• End: Square #0 (Clear painting).
• Rule: You don’t need to jump on every square; you can take giant leaps. For example, jump 20 squares at a time.

You have a task list (Timesteps Schedule) telling you which square to land on next. Suppose you want to finish these 1000 squares in 10 steps.

The schedule might look like this: [999, 899, 799, ..., 99, 0]

During this process, the AI needs to do two things:

1. Observe: What does the current square look like?
2. Calculate: What should the next target square look like?

The key to DDIM Trailing lies here: When calculating the next square, is there a slight deviation between the “time scale” I reference and the actual “physical landing spot” I jump to?

Comparing the Two Modes

Let’s use a more everyday analogy: Bus Stop Announcements.

A. No Trailing (Standard / Leading): “As Is”

This is like being a bus driver. You have to announce a stop every 10 minutes.

• Current time: 10:00.
• System Logic: “I am currently at the 10:00 station exactly. Please calculate the route to 09:50.”
• This is a very intuitive, standard alignment. It is the default logic for many modern samplers.

B. Trailing: Referencing Backwards

DDIM Trailing is an early, specific implementation logic (originating from the original DDIM codebase). Its logic is a bit like using a slightly delayed old watch.

• Physically, you are at the 10:00 station.
• However, when the algorithm grabs parameters, it actually references the previous step’s trailing index. It’s like saying: “Although I am at this station, when I calculate the span, I count from the tail end of the previous interval.”

Mathematically, if you are compressing 1,000 original steps into 50 steps:

• When Trailing is On: The sequence of time steps generated might feel like the sampling endpoint is “trailing” behind. When converting continuous time (0 to 1) to discrete steps (0 to 1000), it uses a form of floor rounding or specific offset.
• Resulting Difference: This causes the AI to handle the very last few steps (when the image is closest to completion) differently.

4. Why Does DDIM Trailing Matter?

You might ask: “Isn’t this just a tiny difference in code? Does it affect me?”

Yes, primarily in terms of ‘Reproduction’ and ‘Determinism’.

1. Determinism: A big selling point of DDIM is that given the same random Seed, the image generated should be identical. However, if your software (like Stable Diffusion WebUI) and someone else’s software differ in their “Trailing” settings, you will get slightly different images (similar composition, but different details), even if all other parameters are the same.
2. Precision of the Last Step: Many studies have found that how Trailing is handled affects whether the final step of image generation converges perfectly to a “pure image.” If handled poorly (e.g., timesteps are misaligned), the image might retain a slight foggy noise, or the brightness and contrast might be slightly off.

5. Conclusion

In most modern AI art software, these complex mathematical details have been optimized and hidden. Current Schedulers mostly use more precise mathematical alignment (like Linspace), and there is usually no need to manually fuss over Trailing.

However, understanding DDIM Trailing helps you realize:

• AI isn’t magic; it’s all math: Even a problem as small as “how to count time steps” affects every stroke of the final painting.
• Trailing is like the rhythm in ‘Restoring Ancient Paintings’: It is that old-school, specific skipping rhythm. Although we now have more precise electronic metronomes, that old-school rhythm is a unique signature of the early DDIM algorithms.

Next time, if you see that a generated image doesn’t quite match the details in a tutorial, perhaps it’s the time ghost named “Trailing” playing a little joke.