LLM Inference Benchmark Explorer
Qwen3.6-35B-A3B on 1× DGX Spark — FP8, vLLM, TP1, speculative decoding k=3 inference benchmark
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| Model | Qwen3.6-35B-A3B |
|---|---|
| Parameters | 35B |
| Intelligence Index | 18.2 |
| Agentic Index | 13.1 |
| Device | 1× DGX Spark |
| Quantization | FP8 |
| Max C | 16 |
| Chat Capacity | 64 |
| Agentic Capacity | 24 |
| TP | — |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 140 | 64.78 | PASS |
| 2 | 347 | 49.22 | PASS |
| 4 | 354 | 30.57 | PASS |
| 8 | 330 | 28.13 | PASS |
| 16 | 464 | 20.68 | PASS |
| 32 | 612 | 14.82 | FAIL |
| 64 | 878 | 10.28 | FAIL |
In OpenZeka's measurement, Qwen3.6-35B-A3B (35B parameters), served in FP8 format with vLLM and speculative decoding on 1× DGX Spark, reached a generation speed of 64.8 tok/s per request and a time to first token (TTFT) of 140 ms with a single request.
Considering the speed targets and the available KV cache capacity, 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: Speculative MTP k=3. +24% TPS at C1 over FP8 without MTP
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Qwen3.6-35B-A3B (35B parameters), served in FP8 format with vLLM and speculative decoding on 1× DGX Spark, reached a generation speed of 64.8 tok/s per request and a time to first token (TTFT) of 140 ms with a single request.
Considering the speed targets and the available KV cache capacity, 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.