Running gpt-oss-120b on MI300X
Quick answer
gpt-oss-120b sustains 8,067 tokens/s per GPU on MI300X at 50 tokens/s per user, which works out to $0.033 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
52
Serving engines
vLLM
Precisions
fp4
Run dates
2026-03-09 → 2026-04-21
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 | 10,570 | $0.025 | vLLM | fp4 |
| 50 tok/s | 8,067 | $0.033 | vLLM | fp4 |
| 75 tok/s | 5,938 | $0.044 | vLLM | fp4 |
| 100 tok/s | 4,659 | $0.057 | vLLM | fp4 |
| 150 tok/s | 3,190 | $0.083 | vLLM | fp4 |
| 200 tok/s | 1,022 | $0.26 | 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 | $0.95 | $0.033 |
| Neocloud | $1.16 | $0.040 |
| Retail | $1.30 | $0.045 |
Frequently asked questions
- How fast is gpt-oss-120b on MI300X?
- At an interactivity target of 50 tokens/s per user on a single-turn chat workload (8k input / 1k output), MI300X sustains 8,067 tokens/s per GPU serving gpt-oss-120b with vLLM in FP4. Peak measured throughput across all configs is 11,449 tokens/s per GPU.
- How much does it cost to serve gpt-oss-120b on MI300X?
- $0.033 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 MI300X?
- 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 MI300X 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 2026-04-21. The same derivation powers the InferenceX overview leaderboard.