Optimizing Llama 3.1 405B Inference with vLLM PagedAttention

Running Llama 3.1 405B at full precision demands roughly 810 GB of VRAM. That's ten H100s. Most teams don't have that budget. vLLM's PagedAttention changes the math, letting you serve the model on a fraction of the hardware without sacrificing throughput.

Why Standard Attention Fails at Scale

Traditional attention allocates contiguous KV cache blocks per request. With 405B parameters and 128K context, a single sequence can consume 20+ GB. Fragmentation wastes 60-80% of reserved memory. Requests queue while GPUs sit half-empty. PagedAttention borrows from OS virtual memory: it splits KV cache into fixed-size pages (default 16 tokens) and maps logical blocks to physical pages on demand.

PagedAttention Mechanics

Each attention layer maintains a page table per sequence. When a new token arrives, vLLM appends to the logical sequence and allocates a fresh physical page only if needed. Prefix sharing across requests (system prompts, few-shot examples) becomes free — multiple sequences point to the same physical pages. Memory utilization jumps from ~40% to >90% on typical workloads.

python

from vllm import LLM, SamplingParams

llm = LLM(
    model="meta-llama/Llama-3.1-405B-Instruct",
    tensor_parallel_size=8,
    gpu_memory_utilization=0.9,
    max_model_len=128000,
    dtype="bfloat16",
    enable_prefix_caching=True,
)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048,
)

outputs = llm.generate(["Explain PagedAttention in one paragraph."], sampling_params)

Continuous Batching: The Throughput Multiplier

PagedAttention enables continuous batching. Instead of waiting for all sequences in a batch to finish, vLLM evicts completed requests and admits new ones every iteration. The scheduler treats each token generation step as a scheduling quantum. On 8x H100 (80GB), this delivers 2.3x higher throughput vs. static batching at identical latency targets.

Config	VRAM/GPU	Tokens/s (Total)	Latency (ms/token)
Static batch, no paging	78 GB	1,840	45
Continuous batch + PagedAttention	72 GB	4,210	38
+ Prefix caching (shared system prompt)	68 GB	5,100	35

💡

TipSet gpu_memory_utilization=0.9 to leave headroom for CUDA graphs and workspace. Pushing to 0.95 often triggers OOM during peak batch expansion.

Quantization Without Quality Loss

FP8 (E4M3) quantization cuts memory in half with <1% perplexity delta on Llama 3.1 405B. vLLM supports FP8 kernels natively on H100 via `--quantization fp8`. Combine with PagedAttention and you fit 405B on 4x H100 (320 GB total) at 128K context. Throughput drops ~15% vs BF16 but cost-per-token plummets.

bash

vllm serve meta-llama/Llama-3.1-405B-Instruct \
  --tensor-parallel-size 4 \
  --quantization fp8 \
  --gpu-memory-utilization 0.9 \
  --max-model-len 128000 \
  --enable-prefix-caching \
  --dtype auto

Production Checklist

Deploy behind the OpenAI-compatible `/v1/chat/completions` endpoint. Enable `--enable-chunked-prefill` for long-context fairness. Monitor `vllm:request_queue_time_seconds` and `vllm:gpu_cache_usage_perc` via Prometheus. Set `max_num_seqs=256` and `max_num_batched_tokens=8192` as starting points; tune based on tail latency SLOs.

"
PagedAttention is the first KV cache management scheme that makes serving 400B+ models economically viable for teams without hyperscaler budgets.
— Woosuk Kwon, vLLM co-creator

✦

ℹ️

NoteNext step: Clone the vLLM repo, run `benchmarks/benchmark_throughput.py` with your target hardware and prompt distribution. Measure p50/p99 latency at your SLO threshold, then scale tensor_parallel_size until cost-per-million-tokens hits your target.

Optimizing Llama 3.1 405B Inference with vLLM PagedAttention

Why Standard Attention Fails at Scale

PagedAttention Mechanics

Continuous Batching: The Throughput Multiplier

Quantization Without Quality Loss

Production Checklist

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