B200: FP4 vs FP8 Precision Comparison
How FP4 and FP8 precision affect MiniMax M3 428B inference on B200 (NVIDIA Blackwell). Throughput, latency, and cost across LLM workloads. Use the chart controls below to switch sequences and metrics — same interactions as the main inference chart.
Near the low end of the 15–389 tok/s/user interactivity band, at 108 tok/s/user on MiniMax M3 428B (B200): FP4 runs 2068 tok/s/GPU at $0.26/M tokens, FP8 runs 1429 at $0.40/M. FP4 is 52% cheaper per token; FP4 delivers 45% more tok/s/GPU. Precision changes affect both inference speed and model quality — consult the evaluation tab for accuracy benchmarks.
At 202 tok/s/user on MiniMax M3 428B (B200), FP4 delivers 942 tok/s/GPU at $0.58 per million tokens; FP8 delivers 542 tok/s/GPU at $1.00. FP4 is 72% cheaper per token; FP4 delivers 74% more tok/s/GPU. Lower-precision quantization trades model accuracy for throughput — check the evaluation page for quality impact.
FP4 posts 375 tok/s/GPU for $1.47 per million tokens at 296 tok/s/user on MiniMax M3 428B (B200); FP8 posts 190 tok/s/GPU for $2.93. FP4 is 100% cheaper per token; FP4 delivers 98% more tok/s/GPU. Quantization-level accuracy differences are tracked on the evaluation tab. (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) | FP4:2068.3FP8:1428.9 | FP4:942.4FP8:542.0 | FP4:375.0FP8:189.8 |
| Cost ($/M tok) | FP4:$0.262FP8:$0.399 | FP4:$0.582FP8:$0.999 | FP4:$1.465FP8:$2.929 |
| tok/s/MW | FP4:953128FP8:658495 | FP4:434277FP8:249770 | FP4:172817FP8:87446 |
| Concurrency | FP4:~64FP8:~34 | FP4:~10FP8:~5 | FP4:~3FP8:~1 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.