Running MiniMax M2.7 on GB300 NVL72
Quick answer
MiniMax M2.7 sustains 13,707 tokens/s per GPU on GB300 NVL72 at 50 tokens/s per user, which works out to $0.047 per million tokens at hyperscaler pricing, served by Dynamo vLLM. Fastest measured TTFT: 0.1 ms; fastest TPOT: 0.0 ms (each the best across all configs, not one run).
Benchmarked configs
74
Serving engines
Dynamo vLLM
Precisions
fp4, fp8
Run dates
2026-06-03 → 2026-06-03
Throughput at every interactivity target
Serving is a trade-off: push more concurrent users through a GPU and each user's tokens arrive slower. The ladder below reads the measured frontier at each per-user speed target on a single-turn chat workload (8k input / 1k output), using the best engine and precision at that point.
| Per-user target | Tokens/s per GPU | $ / 1M tokens | Engine | Precision |
|---|---|---|---|---|
| 30 tok/s | 15,175 | $0.042 | Dynamo vLLM | fp4 |
| 50 tok/s | 13,707 | $0.047 | Dynamo vLLM | fp4 |
| 75 tok/s | 10,749 | $0.060 | Dynamo vLLM | fp4 |
| 100 tok/s | 6,611 | $0.097 | Dynamo vLLM | fp4 |
| 150 tok/s | 2,163 | $0.30 | Dynamo vLLM | fp4 |
| 200 tok/s | - | - | Dynamo vLLM | fp4 |
What serving actually costs
Converting the 50 tokens/s per user operating point to $ per million total tokens across rental pricing tiers from the SemiAnalysis AI Cloud TCO model.
| Pricing tier | $ / GPU / hr | $ / 1M tokens |
|---|---|---|
| Hyperscaler | $2.31 | $0.047 |
| Neocloud | $2.79 | $0.057 |
| Retail | $3.30 | $0.067 |
Frequently asked questions
- How fast is MiniMax M2.7 on GB300 NVL72?
- At an interactivity target of 50 tokens/s per user on a single-turn chat workload (8k input / 1k output), GB300 NVL72 sustains 13,707 tokens/s per GPU serving MiniMax M2.7 with Dynamo vLLM in FP4. Peak measured throughput across all configs is 25,740 tokens/s per GPU.
- How much does it cost to serve MiniMax M2.7 on GB300 NVL72?
- $0.047 per million total tokens at hyperscaler $/GPU/hr pricing, at 50 tokens/s per user. Neocloud and retail rental tiers are tabulated above; slower interactivity targets lower the cost further.
- Which serving engines run MiniMax M2.7 on GB300 NVL72?
- The runs behind this page used Dynamo vLLM in FP4, FP8, including disaggregated prefill and multi-node serving. Engines are rebuilt and re-benchmarked continuously, so the best config can change between visits.
- How are these MiniMax M2.7 numbers measured?
- Every number is measured on real GB300 NVL72 hardware by the InferenceX fleet, sweeping concurrency on a single-turn chat workload (8k input / 1k output) to trace the throughput-versus-interactivity frontier; the newest run landed on 2026-06-03. The same derivation powers the InferenceX overview leaderboard.