LLM Inference Benchmark Explorer
Qwen3.8-Flash-Next on RTX PRO 6000 — NVFP4, SGLang, TP1, speculative decoding k=3 inference benchmark
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| Model | Qwen3.8-Flash-Next |
|---|---|
| Parameters | 176B |
| Intelligence Index | 39.8 |
| Agentic Index | 53.6 |
| Device | RTX PRO 6000 |
| Quantization | NVFP4 |
| Max C | 16 |
| Chat Capacity | 64 |
| Agentic Capacity | 24 |
| TP | — |
| DP | — |
| PP | — |
| Engine | SGLang |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 139 | 155.77 | PASS |
| 2 | 196 | 119.96 | PASS |
| 4 | 221 | 90.76 | PASS |
| 8 | 252 | 63.69 | PASS |
| 16 | 257 | 43.19 | PASS |
| 32 | 3223 | 22.19 | FAIL |
In OpenZeka's measurement, Qwen3.8-Flash-Next (176B parameters), served in NVFP4 format with SGLang and speculative decoding on RTX PRO 6000, reached a generation speed of 155.8 tok/s per request and a time to first token (TTFT) of 139 ms with a single request.
Considering the speed targets alone (the KV cache limit was not calculated for this configuration), the estimated capacity is 64 users for chat use and 24 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: NEXTN MTP k=3. PLE N-gram table (47.7 GiB) in pinned host RAM. mem-frac 0.96, max 16 running; C=32 queues.
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Qwen3.8-Flash-Next (176B parameters), served in NVFP4 format with SGLang and speculative decoding on RTX PRO 6000, reached a generation speed of 155.8 tok/s per request and a time to first token (TTFT) of 139 ms with a single request.
Considering the speed targets alone (the KV cache limit was not calculated for this configuration), the estimated capacity is 64 users for chat use and 24 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.