LLM Inference Benchmark Explorer
GPT-OSS 120B on RTX PRO 6000 — MXFP4, vLLM, TP1 inference benchmark
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| Model | GPT-OSS 120B |
|---|---|
| Parameters | 120B |
| Intelligence Index | 11.6 |
| Agentic Index | 3.7 |
| Device | RTX PRO 6000 |
| Quantization | MXFP4 |
| Max C | 64+ |
| Chat Capacity | 55 |
| Agentic Capacity | 13 |
| TP | — |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 52 | 169.64 | PASS |
| 2 | 59 | 125.81 | PASS |
| 4 | 64 | 98.77 | PASS |
| 8 | 80 | 76.57 | PASS |
| 16 | 122 | 55.27 | PASS |
| 32 | 160 | 41.88 | PASS |
| 64 | 206 | 31.76 | PASS |
In OpenZeka's measurement, GPT-OSS 120B (120B parameters), served in MXFP4 format with vLLM on RTX PRO 6000, reached a generation speed of 169.6 tok/s per request and a time to first token (TTFT) of 52 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 55 users for chat use and 13 for agentic use. The realistic capacity will likely fall between these two values. In scenarios dominated by coding, tool use, long workflows and multi-agent use, capacity approaches the agentic estimate; where shorter interactions, standard conversations and lighter tasks dominate, it approaches the chat estimate.
Notes: 4K context
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, GPT-OSS 120B (120B parameters), served in MXFP4 format with vLLM on RTX PRO 6000, reached a generation speed of 169.6 tok/s per request and a time to first token (TTFT) of 52 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 55 users for chat use and 13 for agentic use. The realistic capacity will likely fall between these two values. In scenarios dominated by coding, tool use, long workflows and multi-agent use, capacity approaches the agentic estimate; where shorter interactions, standard conversations and lighter tasks dominate, it approaches the chat estimate.