LLM Inference Optimization

Efficient inference is critical for deploying language models in production. The autoregressive nature of LLMs—generating one token at a time—creates unique optimization challenges. This tutorial covers the core techniques for maximizing throughput and minimizing latency.

The Inference Challenge

LLM inference has two distinct phases:

1. Prefill phase: Process the entire input prompt in parallel (compute-bound)
2. Decode phase: Generate tokens one at a time (memory-bandwidth-bound)

KV Cache

The KV cache is the most fundamental optimization for autoregressive generation. Instead of recomputing attention for all previous tokens at each step, we cache key-value pairs.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

class KVCacheModel:
    def __init__(self, model_name: str):
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.kv_cache = None
    
    def prefill(self, prompt: str):
        """Process entire prompt and cache KV pairs."""
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
        
        with torch.no_grad():
            outputs = self.model(**inputs, use_cache=True)
            self.kv_cache = outputs.past_key_values
        
        return outputs.logits[:, -1, :]
    
    def decode_step(self, input_ids: torch.Tensor):
        """Generate next token using cached KV pairs."""
        with torch.no_grad():
            outputs = self.model(
                input_ids,
                past_key_values=self.kv_cache,
                use_cache=True
            )
            self.kv_cache = outputs.past_key_values
        
        next_token_logits = outputs.logits[:, -1, :]
        next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
        return next_token
    
    def generate(self, prompt: str, max_new_tokens: int = 100):
        """Full generation with KV cache."""
        self.kv_cache = None
        logits = self.prefill(prompt)
        
        next_token = torch.argmax(logits[:, -1:], dim=-1)
        generated = [next_token.item()]
        
        for _ in range(max_new_tokens - 1):
            next_token = self.decode_step(next_token)
            generated.append(next_token.item())
        
        return self.tokenizer.decode(generated)

Quantization

Quantization reduces model size and accelerates inference by using lower-precision representations.

Quantization Methods Comparison

GPTQ Quantization

GPTQ (GPT Quantization) uses second-order information to quantize weights with minimal accuracy loss:

AWQ (Activation-Aware Weight Quantization)

AWQ identifies salient weight channels based on activation magnitudes and preserves them during quantization:

import torch
from awq import AutoAWQForCausalLM

def quantize_with_awq(model_path: str, output_path: str):
    model = AutoAWQForCausalLM.from_pretrained(model_path)
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
    
    quant_config = {
        "zero_point": True,
        "q_group_size": 128,
        "w_bit": 4,
        "version": "GEMM"
    }
    
    model.quantize(
        tokenizer,
        quant_config=quant_config,
        calib_data="dataset"
    )
    
    model.save_quantized(output_path)
    tokenizer.save_pretrained(output_path)

Speculative Decoding

Speculative decoding uses a smaller "draft" model to propose tokens that are verified in parallel by the target model.

class SpeculativeDecoder:
    def __init__(self, target_model, draft_model, tokenizer, gamma: int = 5):
        self.target = target_model
        self.draft = draft_model
        self.tokenizer = tokenizer
        self.gamma = gamma  # max draft tokens per step
    
    def generate(self, prompt: str, max_new_tokens: int = 100):
        input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
        generated = input_ids.clone()
        
        while generated.shape[1] - input_ids.shape[1] < max_new_tokens:
            # Draft phase: generate gamma tokens with draft model
            draft_tokens = []
            draft_probs = []
            x = generated.clone()
            
            for _ in range(self.gamma):
                with torch.no_grad():
                    logits = self.draft(x).logits[:, -1, :]
                    probs = torch.softmax(logits, dim=-1)
                    token = torch.multinomial(probs, 1)
                    draft_tokens.append(token)
                    draft_probs.append(probs)
                    x = torch.cat([x, token], dim=-1)
            
            # Verification phase: run target model on all draft tokens
            with torch.no_grad():
                target_logits = self.target(x).logits
            
            # Accept or reject each draft token
            n_accepted = 0
            for i in range(self.gamma):
                target_prob = torch.softmax(target_logits[:, -1-i, :], dim=-1)
                draft_prob = draft_probs[i]
                token = draft_tokens[i]
                
                accept_prob = torch.min(
                    torch.ones(1),
                    target_prob[0, token[0, 0]] / draft_prob[0, token[0, 0]]
                )
                
                if torch.rand(1) < accept_prob:
                    n_accepted += 1
                else:
                    break
            
            # Add accepted tokens
            accepted = torch.cat(draft_tokens[:n_accepted], dim=-1)
            generated = torch.cat([generated, accepted], dim=-1)
            
            # Sample one more token from target if not at max
            if generated.shape[1] - input_ids.shape[1] < max_new_tokens:
                with torch.no_grad():
                    target_sample = torch.softmax(target_logits[:, -1, :], dim=-1)
                    new_token = torch.multinomial(target_sample, 1)
                    generated = torch.cat([generated, new_token], dim=-1)
        
        return self.tokenizer.decode(generated[0])

Continuous Batching

Continuous batching (also called in-flight batching) allows dynamic request scheduling instead of static batch processing.

class ContinuousBatchScheduler:
    def __init__(self, model, max_batch_size: int = 32, max_tokens: int = 4096):
        self.model = model
        self.max_batch_size = max_batch_size
        self.max_tokens = max_tokens
        self.pending_requests = []
        self.active_requests = []
    
    def add_request(self, request):
        self.pending_requests.append(request)
    
    def schedule_step(self):
        # Fill batch from pending requests
        while (len(self.active_requests) < self.max_batch_size and 
               self.pending_requests):
            request = self.pending_requests.pop(0)
            if self._can_add(request):
                self.active_requests.append(request)
        
        # Run one decode step for all active requests
        if self.active_requests:
            self._decode_step()
            
            # Remove completed requests
            completed = [r for r in self.active_requests if r.is_done]
            for r in completed:
                self.active_requests.remove(r)
    
    def _can_add(self, request):
        total_tokens = sum(r.current_length for r in self.active_requests)
        return total_tokens + request.current_length <= self.max_tokens
    
    def _decode_step(self):
        # Batch all active requests together
        batch = self._prepare_batch()
        with torch.no_grad():
            logits = self.model(batch.input_ids).logits
        
        for i, request in enumerate(self.active_requests):
            next_token = torch.argmax(logits[i, -1, :])
            request.append_token(next_token)

Deployment Frameworks

vLLM

vLLM pioneered PagedAttention for efficient KV cache management:

from vllm import LLM, SamplingParams

# Initialize vLLM engine
llm = LLM(
    model="meta-llama/Llama-2-7b-hf",
    tensor_parallel_size=1,
    gpu_memory_utilization=0.9,
    max_model_len=4096
)

# Batch inference
prompts = [
    "Explain quantum computing in simple terms.",
    "Write a Python function to sort a list.",
    "What are the benefits of exercise?"
]

sampling_params = SamplingParams(
    temperature=0.8,
    top_p=0.95,
    max_tokens=256
)

outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    print(output.outputs[0].text)

TensorRT-LLM

TensorRT-LLM provides NVIDIA-optimized inference:

import tensorrt_llm

# Build engine
builder = tensorrt_llm.Builder()
network = builder.create_network()
# ... build network architecture ...

# Optimize for inference
config = builder.create_builder_config()
config.max_batch_size = 32
config.max_input_len = 2048
config.max_output_len = 512

engine = builder.build_serialized_network(network, config)

Latency vs Throughput Tradeoffs

Practical Deployment Example

from vllm import LLM, SamplingParams
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn

app = FastAPI()

# Initialize optimized model
llm = LLM(
    model="TheBloke/Llama-2-7B-Chat-GPTQ",
    quantization="gptq",
    tensor_parallel_size=1,
    gpu_memory_utilization=0.85,
    max_model_len=2048,
    enforce_eager=True  # Disable CUDA graphs for lower latency
)

class GenerationRequest(BaseModel):
    prompt: str
    max_tokens: int = 256
    temperature: float = 0.7

@app.post("/generate")
async def generate(request: GenerationRequest):
    params = SamplingParams(
        temperature=request.temperature,
        top_p=0.95,
        max_tokens=request.max_tokens
    )
    
    outputs = llm.generate([request.prompt], params)
    return {
        "text": outputs[0].outputs[0].text,
        "tokens": len(outputs[0].outputs[0].token_ids),
        "usage": {
            "prompt_tokens": len(outputs[0].prompt_token_ids),
            "completion_tokens": len(outputs[0].outputs[0].token_ids)
        }
    }

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Summary

Practice Exercises

1. KV Cache Analysis: Calculate KV cache memory for LLaMA-2-7B, LLaMA-2-13B, and LLaMA-2-70B at sequence length 4096. What batch sizes are feasible on a 24GB GPU?

2. Quantization Benchmark: Compare inference speed and quality between FP16, INT8, and INT4 (GPTQ) for a 7B model. Measure perplexity on WikiText-2 and tokens/second.

3. Speculative Decoding: Implement speculative decoding with a 7B target model and 1.5B draft model. Measure acceptance rate and speedup.

4. Throughput Optimization: Deploy a model with vLLM and measure throughput at different batch sizes. Find the optimal configuration for your hardware.

5. Latency Optimization: Optimize a model for minimum first-token latency using quantization, KV cache, and CUDA graphs. Measure the impact of each optimization.

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