LLM Inference Benchmark Explorer
MiniMax-M3.1-Flash on DGX B300 — NVFP4, vLLM, TP2, speculative decoding k=8 inference benchmark
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| Model | MiniMax-M3.1-Flash |
|---|---|
| Parameters | 428B |
| Intelligence Index | — |
| Agentic Index | — |
| Device | DGX B300 |
| Quantization | NVFP4 |
| Max C | 128 |
| Chat Capacity | 192 |
| Agentic Capacity | 48 |
| TP | 2 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 42 | 299.70 | PASS |
| 2 | 70 | 228.68 | PASS |
| 4 | 81 | 182.17 | PASS |
| 8 | 104 | 137.59 | PASS |
| 16 | 141 | 106.64 | PASS |
| 32 | 201 | 79.62 | PASS |
| 64 | 293 | 44.20 | PASS |
| 128 | 528 | 20.62 | PASS |
| 256 | 633 | 14.12 | FAIL |
In OpenZeka's measurement, MiniMax-M3.1-Flash (428B parameters), served in NVFP4 format with vLLM and speculative decoding on DGX B300 (TP=2), reached a generation speed of 299.7 tok/s per request and a time to first token (TTFT) of 42 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 192 users for chat use and 48 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: DSpark k=8, 1M context. MXFP8 dense layers, NVFP4 experts. Model: MiniMaxAI/MiniMax-M3.1-Flash
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, MiniMax-M3.1-Flash (428B parameters), served in NVFP4 format with vLLM and speculative decoding on DGX B300 (TP=2), reached a generation speed of 299.7 tok/s per request and a time to first token (TTFT) of 42 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 192 users for chat use and 48 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.