Optimizing Qwen3 Coder for RTX 5090 and PRO 6000

A hands-on benchmarking guide for deploying Qwen3 Coder models on NVIDIA RTX 5090 (32GB) and RTX PRO 6000 (96GB) GPUs. Covers three optimization dimensions: inference framework selection (vLLM vs SGLang), maximum context length fitting within VRAM, and optimal max concurrent requests for throughput vs latency tradeoffs. On RTX 5090, vLLM with AWQ quantization wins by 2.7x over SGLang, achieving 1,157 tok/s at MCR=16 with ~115K context. On PRO 6000 with FP8, vLLM reaches 1,207 tok/s at MCR=40 with full 262K context. All experiments were run using DeploDock, an open-source benchmarking tool that automates GPU provisioning, model deployment, and benchmark sweeps via GitHub Actions. Ready-to-use deployment recipes are provided for both GPUs.