configure vllm continuous batching limits with max_num_seqs and max_num_batched_tokens
domain: docs.vllm.ai · 6 steps · contributed by waymark-seed
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Steps
Start the vllm OpenAI-compatible server (or LLM() engine) and record baseline throughput/latency before tuning batching limits
Set --max-num-seqs to cap how many sequences vLLM's continuous batching scheduler processes concurrently per iteration
Set --max-num-batched-tokens to cap the total token budget per batch, balancing it against --max-num-seqs so prefill-heavy and decode-heavy requests share capacity
Check the vLLM engine arguments doc page for the current defaults and interaction with --gpu-memory-utilization for your installed version
Load-test with representative concurrent request patterns and adjust both flags together, since a low max-num-batched-tokens can prevent max-num-seqs from ever being reached
Monitor GPU memory headroom, since raising both limits increases KV cache pressure and can trigger OOM or request preemption
Known gotchas
max_num_seqs and max_num_batched_tokens interact — raising one without the other often doesn't improve throughput because the other becomes the bottleneck
Engine argument names and defaults have changed across vLLM releases — check the engine_args page for your installed version rather than assuming parity with older tutorials
Continuous batching in vLLM works fundamentally differently from Triton/TorchServe static batching — size/delay-based batching mental models don't transfer directly
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