LLM Inference Benchmark Explorer
DeepSeek-V4.1-Flash on 8× DGX Spark — FP8, vLLM, TP8, speculative decoding k=5 inference benchmark
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| Model | DeepSeek-V4.1-Flash |
|---|---|
| Parameters | 763B |
| Intelligence Index | 39.5 |
| Agentic Index | — |
| Device | 8× DGX Spark |
| Quantization | FP8 |
| Max C | 4 |
| Chat Capacity | 16 |
| Agentic Capacity | 6 |
| TP | 8 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 213 | 35.98 | PASS |
| 2 | 280 | 28.21 | PASS |
| 4 | 370 | 20.10 | PASS |
| 8 | 488 | 13.63 | FAIL |
In OpenZeka's measurement, DeepSeek-V4.1-Flash (763B parameters), served in FP8 format with vLLM and speculative decoding on 8× DGX Spark (TP=8), reached a generation speed of 36 tok/s per request and a time to first token (TTFT) of 213 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 16 users for chat use and 6 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 k=5, 300K context, Engram in memory (stock vLLM). Model: deepseek-ai/DeepSeek-V4.1-Flash
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, DeepSeek-V4.1-Flash (763B parameters), served in FP8 format with vLLM and speculative decoding on 8× DGX Spark (TP=8), reached a generation speed of 36 tok/s per request and a time to first token (TTFT) of 213 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 16 users for chat use and 6 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.