Running gpt-oss-120b on H100
Quick answer
gpt-oss-120b sustains 8,973 tokens/s per GPU on H100 at 50 tokens/s per user, which works out to $0.036 per million tokens at hyperscaler pricing, served by vLLM. Fastest measured TTFT: 0.0 ms; fastest TPOT: 0.0 ms (each the best across all configs, not one run).
Benchmarked configs
43
Serving engines
vLLM
Precisions
fp4
Run dates
2026-03-27 → 2026-05-17
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 | - | - | vLLM | fp4 |
| 50 tok/s | 8,973 | $0.036 | vLLM | fp4 |
| 75 tok/s | 7,136 | $0.046 | vLLM | fp4 |
| 100 tok/s | 5,863 | $0.055 | vLLM | fp4 |
| 150 tok/s | 3,931 | $0.083 | vLLM | fp4 |
| 200 tok/s | 2,558 | $0.13 | 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 | $1.17 | $0.036 |
| Neocloud | $1.55 | $0.048 |
| Retail | $1.78 | $0.055 |
Frequently asked questions
- How fast is gpt-oss-120b on H100?
- At an interactivity target of 50 tokens/s per user on a single-turn chat workload (8k input / 1k output), H100 sustains 8,973 tokens/s per GPU serving gpt-oss-120b with vLLM in FP4. Peak measured throughput across all configs is 10,284 tokens/s per GPU.
- How much does it cost to serve gpt-oss-120b on H100?
- $0.036 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 gpt-oss-120b on H100?
- The runs behind this page used vLLM in FP4. Engines are rebuilt and re-benchmarked continuously, so the best config can change between visits.
- How are these gpt-oss-120b numbers measured?
- Every number is measured on real H100 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 2026-05-17. The same derivation powers the InferenceX overview leaderboard.