0ba4cdb025
256 concurrent seqs risks OOM: worst case with long prompts in queue can exhaust KV cache + activation memory. 32K batched-tokens prefill activation ≈ 20GB competes with KV cache. 0.95 mem-util leaves only 5% headroom for spikes. Conservative start: max-num-seqs=8 (8× improvement over baseline=1). 8 seqs × 2048 avg context × 80KB/token = 1.3GB KV cache, safe. gpu-memory-utilization and max-num-batched-tokens restored to proven baseline values. Optimal max-num-seqs needs real-hardware sweep: 4→8→16→32→64→128. The value where Output TPS plateaus (KV cache saturated) is the answer. Can't determine this without Phanthy Cloud access.
35 lines
707 B
YAML
35 lines
707 B
YAML
concurrency: 1
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command:
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- python3
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- -m
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- vllm.entrypoints.openai.api_server
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- --model
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- /model
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- --served-model-name
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- llm
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- --max-model-len
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- '100000'
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- --gpu-memory-utilization
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- '0.9'
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- --trust-remote-code
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- -tp
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- '4'
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- --max-num-seqs
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- '8'
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- --disable-log-requests
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- --disable-frontend-multiprocessing
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- --max-num-batched-tokens
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- '8192'
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- --enable-chunked-prefill
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- --max-seq-len-to-capture
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- '32768'
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- --enable-auto-tool-choice
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- --tool-call-parser
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- qwen3_coder
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- --reasoning-parser
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- qwen3
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- --enable-prefix-caching
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env:
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- name: VLLM_ENGINE_ITERATION_TIMEOUT_S
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value: 3600
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