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name: deployment-performance description: "Deployment topology, parallelism, memory planning, quantization, KV cache/offloading, disaggregated serving, distributed execution, observability, profiling, environment collection, backend/hardware selection, and performance troubleshooting for vLLM." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Deployment Performance

Use this sub-skill when a task asks how to deploy, scale, tune, observe, or troubleshoot vLLM performance. It owns operational configuration and diagnosis, not request payload syntax.

Route Here For

• Choosing vllm serve engine flags for GPU memory, context length, batch size, tensor/data/pipeline/expert/context parallelism, Ray, multiprocessing, or torchrun-style external launchers.
• Planning quantization, dtype, CPU weight offload, KV cache sizing, prefix caching, KV offloading, or disaggregated prefill/decode topologies.
• Setting up production metrics, OpenTelemetry tracing, Prometheus/Grafana scraping, profiler captures, benchmark interpretation, or environment summaries.
• Debugging OOM, unsupported dtype/quantization, CUDA/ROCm/CPU mismatch, NCCL/GLOO/Ray/network failures, bad parallel sizes, missing /metrics, or misleading throughput results.

Start With These References

• Configuration: engine/server flags, memory controls, quantization, KV cache, prefix cache, and profiling knobs.
• Deployment: single-node, multi-node, Ray, multiprocessing, data/expert/context parallel, disaggregated prefill, and hardware/backend selection.
• Performance and Observability: metrics, OpenTelemetry, profiling, benchmarks, and runbook signals.
• Troubleshooting: symptom-to-action checks for memory, distributed launch, hardware, metrics, and benchmark issues.

Bundled Tools

• scripts/collect_env_summary.py: safe environment and package summary for support tickets; does not download models or start servers.
• scripts/memory_command_planner.py: prints a conservative vllm serve command skeleton and checklist from model length, GPU count, TP size, memory utilization, offload, and KV-cache inputs.

Cross-Skill Routing

• For offline LLM.generate code and sampling/request examples, route to the offline-inference sub-skill.
• For OpenAI-compatible API request bodies, chat/completions syntax, or client integration, route to the openai-serving sub-skill.
• For structured outputs, tool calling, parser behavior, or guided decoding interactions, route to structured-tool-reasoning and cross-link back here only for throughput/profiling impact.
• For LoRA, multimodal, pooling, or adapter-specific memory details, route to modalities-adapters-pooling and cross-link back here for GPU/KV/cache capacity planning.