vLLM vs. TGI: The Architecture Masterclass on PagedAttention and Local LLM Serving

vLLM vs. TGI: The Architecture Masterclass on PagedAttention and Local LLM Serving

1. Executive Overview: The Key-Value (KV) Cache Bottleneck

When running large language models in production, the primary bottleneck during multi-turn conversations and high-concurrency requests is not raw compute—it is GPU memory allocation for the Key-Value (KV) cache. Traditional serving engines allocate contiguous blocks of physical VRAM based on maximum sequence lengths (e.g., 4096 tokens). Because most queries are much shorter, up to 60–80% of GPU memory sits idle due to internal and external fragmentation.

2. The Architectural Solution: PagedAttention

Inspired by virtual memory paging in operating systems, PagedAttention breaks the KV cache into fixed-size blocks (typically 16 tokens per block). These blocks do not need to be contiguous in physical GPU memory. A lookup table translates logical sequence positions into physical memory slots, allowing the server to batch requests dynamically and recover up to 96% of previously wasted VRAM.

3. Production Implementation: Deploying vLLM with Python

from vllm import AsyncLLMEngine, AsyncEngineArgs, SamplingParams
import asyncio

engine_args = AsyncEngineArgs(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    tensor_parallel_size=1,
    gpu_memory_utilization=0.92,
    max_model_len=8192,
    enable_chunked_prefill=True,
    swap_space=4
)

engine = AsyncLLMEngine.from_engine_args(engine_args)
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=1024)

async def generate_response(prompt_text: str):
    request_id = "req-001"
    results_generator = engine.generate(prompt_text, sampling_params, request_id)
    final_output = None
    async for request_output in results_generator:
        final_output = request_output
    return final_output.outputs[0].text

output = asyncio.run(generate_response("Explain Zero-Knowledge proofs in 3 bullet points."))
print(output)

Key Architectural Takeaways:

• Enable enable_chunked_prefill=True in vLLM to prevent massive context inputs from starving ongoing streaming requests.
• Set gpu_memory_utilization between 0.90 and 0.95 to balance maximum batch size against CUDA out-of-memory risks.
• Use vLLM for high-throughput multi-user APIs; use TGI if your infrastructure is heavily coupled with Hugging Face Hub workflows.