@aryan10582568535

ROCm was the enabling layer for the entire project. Setting up vLLM on ROCm 6.x was significantly smoother than anticipated — gpu-memory-utilization flags worked correctly, bfloat16 precision loaded cleanly on MI300X, and both models (0.12 + 0.55 = 0.67 utilization) coexisted without conflict. The 192 GB unified pool made it trivial to set explicit VRAM budgets per model without worrying about fragmentation. One friction point: ROCm's documentation for multi-model concurrent serving with vLLM could be more explicit. I had to empirically discover the safe utilization ceiling rather than find it in the docs. AMD Developer Cloud provided low-latency access to MI300X hardware with no cold-start surprises. Provisioning was fast, SSH access was reliable, and the hardware behaved exactly as the specs suggested — the full 192 GB was addressable and stable under sustained dual-model inference load. The benchmark numbers in the README (18,397 ms / 40,182 ms) are real, captured live on the Cloud instance. A persistent storage option (beyond the session) and a simple web dashboard for monitoring live GPU utilization would make it significantly easier to iterate during a hackathon. AMD APIs / vLLM + ROCm: The OpenAI-compatible /v1/chat/completions endpoint that vLLM exposes on ROCm worked perfectly with standard Python httpx clients — zero code changes needed compared to CUDA-based setups. Prometheus metrics (/metrics) exported by the Rust engine confirmed consistent GPU throughput across both model instances. Overall the developer experience was solid; the main ask is better first-party documentation specifically for concurrent multi-model deployments, which is the exact use case that makes AMD hardware uniquely powerful.

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