Serving Llama 3.1 on AMD MI300X: How vLLM Crushes TGI Benchmarks

Meta recently confirmed they are running 100% of their live Llama 3.1 405B model traffic on AMD MI300X GPUs. With the release of ROCm 6.2, AMD's ecosystem is no longer just an alternative—it is a production-ready powerhouse.

But hardware is only half the equation. To maximize your ROI on AMD Instinct™ accelerators, your inference engine needs to be perfectly tuned.

In collaboration with Hot Aisle Inc., our infrastructure team extensively benchmarked vLLM against Text Generation Inference (TGI) on 8x MI300X configurations. The results establish a clear new standard for open-source LLM serving.

The Benchmark Breakthroughs

By properly orchestrating vLLM on ROCm, we unlocked massive performance gains over TGI:

• Llama 3.1 405B: Achieved 1.5x higher throughput and a 1.7x faster Time-To-First-Token (TTFT).
• Llama 3.1 70B: Achieved 1.8x higher throughput and a staggering 5.1x faster TTFT.

The MI300X Optimization Playbook

Out-of-the-box settings leave too much performance on the table. To achieve these metrics, we identified 8 critical configuration shifts specific to the MI300X architecture, including:

• CK Flash Attention: Bypassing the Triton implementation for significantly faster native processing.
• Multi-Step Scheduling: Setting scheduler steps between 10–15 to dramatically reduce GPU idle time and CPU overhead.
• Chunked Prefill Optimization: Counterintuitively, disabling this experimental feature on the MI300X currently yields more consistent, higher baseline performance for most workloads.
• Environmental Tuning: Disabling NUMA balancing at the kernel level to prevent GPU hangs and optimize NCCL communication channels.

If you are deploying frontier models on AMD hardware, operating with default parameters is costing you throughput.

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