Optimize inference speed with batching, caching, and specialized runtimes.

KV Cache Optimization

PagedAttention (vLLM)

from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-2-7b-hf",
    tensor_parallel_size=2,
    gpu_memory_utilization=0.9
)

# Efficient batched inference
sampling_params = SamplingParams(temperature=0.7, max_tokens=256)
outputs = llm.generate(prompts, sampling_params)

Continuous Batching

Traditional: Wait for batch to complete Continuous: Process requests as they finish

Benefits:

• 2-3x throughput improvement
• Lower latency for short requests
• Better GPU utilization

Speculative Decoding

class SpeculativeDecoder:
    def __init__(self, target_model, draft_model):
        self.target = target_model  # Large, accurate
        self.draft = draft_model    # Small, fast

    def generate(self, prompt, k=4):
        tokens = []

        while not done:
            # Draft model generates k tokens quickly
            draft_tokens = self.draft.generate(prompt, num_tokens=k)

            # Target model verifies in parallel
            accepted = self.target.verify(prompt, draft_tokens)

            tokens.extend(accepted)
            prompt = prompt + accepted

        return tokens

Hardware-Specific Optimization

TensorRT (NVIDIA)

import tensorrt as trt

# Convert model to TensorRT
builder = trt.Builder(logger)
network = builder.create_network()
parser = trt.OnnxParser(network, logger)
parser.parse_from_file("model.onnx")

engine = builder.build_cuda_engine(network)
# 2-4x faster inference on NVIDIA GPUs