MI355X: FP4 vs BF16 Precision Comparison
How FP4 and BF16 precision affect Qwen 3.5 397B-A17B inference on MI355X (AMD CDNA 4). Throughput, latency, and cost across LLM workloads. Use the chart controls below to switch sequences and metrics — same interactions as the main inference chart.
At 64 tok/s/user on Qwen 3.5 397B-A17B (MI355X), FP4 delivers 1573 tok/s/GPU at $0.26 per million tokens; BF16 delivers 879 tok/s/GPU at $0.47. FP4 is 82% cheaper per token; FP4 delivers 79% more tok/s/GPU. Lower-precision quantization trades model accuracy for throughput — check the evaluation page for quality impact.
FP4 posts 1071 tok/s/GPU for $0.38 per million tokens at 99 tok/s/user on Qwen 3.5 397B-A17B (MI355X); BF16 posts 597 tok/s/GPU for $0.70. FP4 is 82% cheaper per token; FP4 delivers 79% more tok/s/GPU. Quantization-level accuracy differences are tracked on the evaluation tab.
Throughput at 133 tok/s/user on Qwen 3.5 397B-A17B (MI355X): FP4 hits 721 tok/s/GPU, BF16 hits 279. Per-million costs land at $0.55 and $1.47 respectively. FP4 is 166% cheaper per token; FP4 delivers 158% more tok/s/GPU. The cost-throughput tradeoff from lower precision is only part of the picture — see the evaluation page for accuracy data. (Numbers reflect the default 1k/1k selection for this URL — table and chart below update if you change sequence or model in the controls. Each side uses the best available serving configuration for that precision, which may include speculative decoding such as MTP where recipes exist — the same convention as the other comparison pages.)

| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | FP4:1572.7BF16:878.7 | FP4:1070.8BF16:596.7 | FP4:721.4BF16:279.3 |
| Cost ($/M tok) | FP4:$0.258BF16:$0.470 | FP4:$0.384BF16:$0.700 | FP4:$0.554BF16:$1.472 |
| tok/s/MW | FP4:593481BF16:331595 | FP4:404079BF16:225160 | FP4:272243BF16:105385 |
| Concurrency | FP4:~25BF16:~128 | FP4:~11BF16:~24 | FP4:~5BF16:~9 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.