env/vllm_version.md

vLLM Version and Per-Model Launch Notes

Version used in paper: vLLM 0.19.0

All five models are served via vllm.entrypoints.openai.api_server. Common defaults across all models:

Parameter	Value
--max-model-len	16384
--gpu-memory-utilization	0.90
--enable-auto-tool-choice	(flag)
--no-scheduler-reserve-full-isl	(flag, required — avoids a vLLM 0.19.0 scheduler deadlock under sustained multi-agent load)
Input context cap	12,000 tokens (set in runner via _input_token_limit)
Max output tokens	1536 (512 for Gemma)
Temperature	0.85
BF16 weights	default

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Per-model deviations

Qwen/Qwen3-32B

• TP=2 (GPUs 0-1)
• --tool-call-parser hermes
• tool_choice=required
• Thinking suppressed via chat_template_kwargs = {"enable_thinking": False}

openai/gpt-oss-20b

• TP=1 (GPU 2); mxfp4 quantized weights fit in ~40 GB
• --tool-call-parser openai
• --reasoning-parser openai_gptoss
• tool_choice=required

mistralai/Magistral-Small-2509

• TP=2 (GPUs 3-4)
• --tokenizer-mode mistral --config-format mistral --load-format mistral (Mistral models require their own loader; standard HuggingFace format fails)
• --tool-call-parser mistral
• --reasoning-parser mistral
• tool_choice=auto — Magistral returns empty tool_calls with required; auto is the correct setting

zai-org/GLM-4-32B-0414

• TP=2 (GPUs 5-6)
• --tool-call-parser hermes
• tool_choice=required — GLM-4 silently returns plain text with auto; this was discovered when warm-up showed only 1/10 agents posting per run

google/gemma-4-31B-it

• TP=2 (GPUs 0-1 or 3-4 depending on node configuration)
• --tool-call-parser gemma4
• --chat-template oasis/tool_chat_template_gemma4.jinja (custom template, required)
• tool_choice=auto — same symptom as Magistral with required
• --max-num-seqs 64 — tuned to avoid KV-cache saturation under concurrent load
• max_tokens=512 (runner-side cap, lower than other models)
• Note: There is a discrepancy between the paper appendix (max_num_seqs=64) and an earlier production launch script (max_num_seqs=8). The appendix value (64) is the final setting used in paper runs.

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Known issues in vLLM 0.19.0

• Scheduler deadlock: Under sustained multi-agent load, the scheduler can deadlock. Mitigated by --no-scheduler-reserve-full-isl on all models.
• Gemma KV-cache saturation: Default scheduling settings cause OOM-like behavior under concurrent requests. --max-num-seqs 64 and ensuring --gpu-memory-utilization 0.90 resolves this.
• Magistral/Gemma tool_choice=required: Both models return empty tool_calls lists when tool_choice=required; the runner uses auto and handles non-tool responses with prose-to-action scaffolding.