LLM Inference Benchmark Explorer
Qwen3-Coder-30B-A3B-Instruct on 1× DGX Spark — NVFP4, vLLM, TP1 inference benchmark
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| Model | Qwen3-Coder-30B-A3B-Instruct |
|---|---|
| Parameters | 30B |
| Intelligence Index | 9.6 |
| Agentic Index | — |
| Device | 1× DGX Spark |
| Quantization | NVFP4 |
| Max C | 32+ |
| Chat Capacity | 99 |
| Agentic Capacity | 24 |
| TP | — |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 66 | 64.99 | PASS |
| 2 | 49 | 53.75 | PASS |
| 4 | 59 | 42.44 | PASS |
| 8 | 76 | 33.59 | PASS |
| 16 | 78 | 27.29 | PASS |
| 32 | 92 | 21.75 | PASS |
In OpenZeka's measurement, Qwen3-Coder-30B-A3B-Instruct (30B parameters), served in NVFP4 format with vLLM on 1× DGX Spark, reached a generation speed of 65 tok/s per request and a time to first token (TTFT) of 66 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 99 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: NVFP4 is 2.2x faster than FP16 at C1
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Qwen3-Coder-30B-A3B-Instruct (30B parameters), served in NVFP4 format with vLLM on 1× DGX Spark, reached a generation speed of 65 tok/s per request and a time to first token (TTFT) of 66 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 99 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.