LLM Inference Benchmark Explorer
MiMo-V2.6-Pro-MOPD on DGX B300 — FP4, vLLM, TP4, speculative decoding k=7 inference benchmark
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| Model | MiMo-V2.6-Pro-MOPD |
|---|---|
| Parameters | 1.02T |
| Intelligence Index | 46.3 |
| Agentic Index | — |
| Device | DGX B300 |
| Quantization | FP4 |
| Max C | 256+ |
| Chat Capacity | 954 |
| Agentic Capacity | 246 |
| TP | 4 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 60 | 316.50 | PASS |
| 2 | 68 | 257.75 | PASS |
| 4 | 80 | 214.21 | PASS |
| 8 | 100 | 163.11 | PASS |
| 16 | 126 | 131.72 | PASS |
| 32 | 172 | 108.05 | PASS |
| 64 | 172 | 86.08 | PASS |
| 128 | 307 | 51.18 | PASS |
| 256 | 535 | 27.91 | PASS |
In OpenZeka's measurement, MiMo-V2.6-Pro-MOPD (1.02T parameters), served in FP4 format with vLLM and speculative decoding on DGX B300 (TP=4), reached a generation speed of 316.5 tok/s per request and a time to first token (TTFT) of 60 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 954 users for chat use and 246 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: DFlash speculative decoding k=7, 1M context. Model: XiaomiMiMo/MiMo-V2.6-Pro-MOPD
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, MiMo-V2.6-Pro-MOPD (1.02T parameters), served in FP4 format with vLLM and speculative decoding on DGX B300 (TP=4), reached a generation speed of 316.5 tok/s per request and a time to first token (TTFT) of 60 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 954 users for chat use and 246 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.