LLM Inference Benchmark Explorer
DeepSeek-V4-Pro-0813 on DGX B300 — FP8, vLLM, TP4 inference benchmark
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| Model | DeepSeek-V4-Pro-0813 |
|---|---|
| Parameters | 1.65T |
| Intelligence Index | 36 |
| Agentic Index | 41.3 |
| Device | DGX B300 |
| Quantization | FP8 |
| Max C | 32 |
| Chat Capacity | 128 |
| Agentic Capacity | 48 |
| TP | 4 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 279 | 117.77 | PASS |
| 2 | 87 | 107.49 | PASS |
| 4 | 284 | 80.85 | PASS |
| 8 | 107 | 72.79 | PASS |
| 16 | 307 | 52.36 | PASS |
| 32 | 314 | 45.17 | PASS |
| 64 | 584 | 16.64 | FAIL |
In OpenZeka's measurement, DeepSeek-V4-Pro-0813 (1.65T parameters), served in FP8 format with vLLM on DGX B300 (TP=4), reached a generation speed of 117.8 tok/s per request and a time to first token (TTFT) of 279 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 128 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: 1M context
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, DeepSeek-V4-Pro-0813 (1.65T parameters), served in FP8 format with vLLM on DGX B300 (TP=4), reached a generation speed of 117.8 tok/s per request and a time to first token (TTFT) of 279 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 128 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.