LLM Inference Benchmark Explorer
Qwen3.8-27B on 1× DGX Spark — NVFP4, vLLM, TP1, speculative decoding inference benchmark
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| Model | Qwen3.8-27B |
|---|---|
| Parameters | 27B |
| Intelligence Index | 33.7 |
| Agentic Index | 45.8 |
| Device | 1× DGX Spark |
| Quantization | NVFP4 |
| Max C | 1+ |
| Chat Capacity | 4+ |
| Agentic Capacity | 1+ |
| TP | — |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 281 | 30.58 | PASS |
In OpenZeka's measurement, Qwen3.8-27B (27B parameters), served in NVFP4 format with vLLM and speculative decoding on 1× DGX Spark, reached a generation speed of 30.6 tok/s per request and a time to first token (TTFT) of 281 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is at least 4 users for chat use and at least 1 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.
This configuration was measured with a single request only, so the estimate rests on one measurement.
Notes: eugr nightly. Single C=1 test (config matrix)
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Qwen3.8-27B (27B parameters), served in NVFP4 format with vLLM and speculative decoding on 1× DGX Spark, reached a generation speed of 30.6 tok/s per request and a time to first token (TTFT) of 281 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is at least 4 users for chat use and at least 1 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.
This configuration was measured with a single request only, so the estimate rests on one measurement.