LLM Inference Benchmark Explorer
Kimi-K2-Thinking on DGX B300 — NVFP4, vLLM, TP4 inference benchmark
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| Model | Kimi-K2-Thinking |
|---|---|
| Parameters | 1.03T |
| Intelligence Index | 22 |
| Agentic Index | — |
| Device | DGX B300 |
| Quantization | NVFP4 |
| Max C | 32+ |
| Chat Capacity | 128+ |
| Agentic Capacity | 42 |
| TP | 4 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 48 | 53.58 | PASS |
| 2 | 62 | 52.89 | PASS |
| 4 | 86 | 51.94 | PASS |
| 8 | 132 | 46.56 | PASS |
| 16 | 316 | 38.46 | PASS |
| 32 | 393 | 30.21 | PASS |
In OpenZeka's measurement, Kimi-K2-Thinking (1.03T parameters), served in NVFP4 format with vLLM on DGX B300 (TP=4), reached a generation speed of 53.6 tok/s per request and a time to first token (TTFT) of 48 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is at least 128 users for chat use and 42 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.
“At least” means the configuration met the speed targets even at the highest load tested, so the real capacity may be higher.
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Kimi-K2-Thinking (1.03T parameters), served in NVFP4 format with vLLM on DGX B300 (TP=4), reached a generation speed of 53.6 tok/s per request and a time to first token (TTFT) of 48 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is at least 128 users for chat use and 42 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.
“At least” means the configuration met the speed targets even at the highest load tested, so the real capacity may be higher.