LLM Inference Benchmark Explorer
Gemma-4-31B-it on 4× DGX Spark — NVFP4, vLLM, TP4 inference benchmark
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| Model | Gemma-4-31B-it |
|---|---|
| Parameters | 31B |
| Intelligence Index | 19 |
| Agentic Index | 4.2 |
| Device | 4× DGX Spark |
| Quantization | NVFP4 |
| Max C | 16 |
| Chat Capacity | 64 |
| Agentic Capacity | 24 |
| TP | 4 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 280 | 30.53 | PASS |
| 2 | 245 | 29.31 | PASS |
| 4 | 306 | 28.25 | PASS |
| 8 | 379 | 26.18 | PASS |
| 16 | 368 | 20.80 | PASS |
| 32 | 480 | 13.93 | FAIL |
| 64 | 565 | 7.94 | FAIL |
In OpenZeka's measurement, Gemma-4-31B-it (31B parameters), served in NVFP4 format with vLLM on 4× DGX Spark (TP=4), reached a generation speed of 30.5 tok/s per request and a time to first token (TTFT) of 280 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.
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Gemma-4-31B-it (31B parameters), served in NVFP4 format with vLLM on 4× DGX Spark (TP=4), reached a generation speed of 30.5 tok/s per request and a time to first token (TTFT) of 280 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.