Optimizing vLLM Throughput with PagedAttention and Continuous Batching for Llama 3

Getting Llama 3 to run at peak throughput isn't just about having enough VRAM; it's about how the KV cache is managed. If you're still running basic inference servers, you're likely wasting 30-50% of your memory on fragmented cache slots. vLLM's implementation of PagedAttention is the actual game-changer here because it treats the KV cache like virtual memory, allowing non-contiguous storage of tokens.

In my current production setup, I noticed that during high-concurrency bursts, the "TTFT" (Time to First Token) was spiking. The culprit was the default block size and a lack of tuning for continuous batching. Continuous batching allows vLLM to insert new requests into the batch the moment another request finishes, rather than waiting for the entire batch to complete.

To actually squeeze the most out of Llama 3, you need to tune the --gpu-memory-utilization and --max-model-len flags carefully. If you set utilization too high (e.g., 0.95), you risk OOMs during peak bursts because the system doesn't have enough headroom for the actual computation.

Here is the deployment command I've found to be the "sweet spot" for a Llama 3 8B instance on an A100 (40GB):

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3-8B \
    --gpu-memory-utilization 0.90 \
    --max-model-len 8192 \
    --max-num-seqs 256 \
    --block-size 16

Key Tuning Insights:

• Block Size: I tried block-size 32, but 16 generally reduced internal fragmentation for shorter responses, leading to slightly higher throughput.
• Max Num Seqs: This is your concurrency ceiling. Pushing this too high will increase your latency per request. I monitor the avg_prompt_throughput vs avg_generation_throughput in the vLLM logs; if the generation throughput drops sharply, I dial this back.
• KV Cache Allocation: By default, vLLM grabs as much memory as possible. If you're running other processes on the same GPU, you must lower --gpu-memory-utilization or the engine will crash on startup.

A common "gotcha" is the interaction between Llama 3's group-query attention (GQA) and the PagedAttention mechanism. Since Llama 3 uses GQA, the KV cache is smaller than in Llama 2, which means you can actually fit significantly more requests into a single batch. If you aren't seeing a massive jump in throughput compared to older models, check if your max-num-seqs is too conservative.

For those using Claude Code or Cursor to write the wrapper scripts for these deployments, I recommend prompting the AI to generate a Prometheus monitoring config. vLLM exposes an /metrics endpoint that is gold for debugging throughput bottlenecks.

If you're seeing "KV cache full" warnings in your logs, don't just throw more hardware at it. Lower your --max-model-len if you don't actually need 8k context, as this directly frees up slots for continuous batching to handle more concurrent users.