LLM Inference Benchmark Explorer
Qwen3.8-2.4T-A95B on DGX B300 — NVFP4, vLLM, TP8 inference benchmark
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| Model | Qwen3.8-2.4T-A95B |
|---|---|
| Parameters | 2.4T |
| Intelligence Index | 39.9 |
| Agentic Index | 50.1 |
| Device | DGX B300 |
| Quantization | NVFP4 |
| Max C | 64+ |
| Chat Capacity | 192 |
| Agentic Capacity | 71 |
| TP | 8 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 113 | 115.87 | PASS |
| 2 | 94 | 104.57 | PASS |
| 4 | 169 | 87.07 | PASS |
| 8 | 192 | 75.53 | PASS |
| 16 | 365 | 58.98 | PASS |
| 32 | 422 | 45.51 | PASS |
| 64 | 719 | 31.30 | PASS |
In OpenZeka's measurement, Qwen3.8-2.4T-A95B (2.4T parameters), served in NVFP4 format with vLLM on DGX B300 (TP=8), reached a generation speed of 115.9 tok/s per request and a time to first token (TTFT) of 113 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 71 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: Model: Inferact/Qwen3.8-2.4T-A95B-NVFP4, 262K context
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Qwen3.8-2.4T-A95B (2.4T parameters), served in NVFP4 format with vLLM on DGX B300 (TP=8), reached a generation speed of 115.9 tok/s per request and a time to first token (TTFT) of 113 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 71 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.