LLM Inference Benchmark Explorer
Muse-Spark-Open-816B-A42B on 8× DGX Spark — NVFP4, vLLM, TP8, speculative decoding inference benchmark
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| Model | Muse-Spark-Open-816B-A42B |
|---|---|
| Parameters | 816B |
| Intelligence Index | 38.9 |
| Agentic Index | — |
| Device | 8× DGX Spark |
| Quantization | NVFP4 |
| Max C | 1 |
| Chat Capacity | 4 |
| Agentic Capacity | 1 |
| TP | 8 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 508 | 24.20 | PASS |
| 2 | 695 | 18.44 | FAIL |
| 4 | 815 | 15.23 | FAIL |
| 8 | 990 | 12.77 | FAIL |
In OpenZeka's measurement, Muse-Spark-Open-816B-A42B (816B parameters), served in NVFP4 format with vLLM and speculative decoding on 8× DGX Spark (TP=8), reached a generation speed of 24.2 tok/s per request and a time to first token (TTFT) of 508 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 4 users for chat use and 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.
Notes: DSpark speculative. 128K context.
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Muse-Spark-Open-816B-A42B (816B parameters), served in NVFP4 format with vLLM and speculative decoding on 8× DGX Spark (TP=8), reached a generation speed of 24.2 tok/s per request and a time to first token (TTFT) of 508 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 4 users for chat use and 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.