LLM Inference Benchmark Explorer
Tencent-Hy4-preview on DGX B300 — BF16, vLLM, TP8, speculative decoding k=3 inference benchmark
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| Model | Tencent-Hy4-preview |
|---|---|
| Parameters | 780B |
| Intelligence Index | — |
| Agentic Index | — |
| Device | DGX B300 |
| Quantization | BF16 |
| Max C | 64+ |
| Chat Capacity | 65 |
| Agentic Capacity | 16 |
| TP | 8 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 110 | 65.66 | PASS |
| 2 | 123 | 61.16 | PASS |
| 4 | 146 | 55.73 | PASS |
| 8 | 190 | 46.80 | PASS |
| 16 | 268 | 38.96 | PASS |
| 32 | 398 | 30.76 | PASS |
| 64 | 476 | 24.81 | PASS |
In OpenZeka's measurement, Tencent-Hy4-preview (780B parameters), served in BF16 format with vLLM and speculative decoding on DGX B300 (TP=8), reached a generation speed of 65.7 tok/s per request and a time to first token (TTFT) of 110 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 65 users for chat use and 16 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: MTP k=3, 512K context. Model: tencent/Hy4-preview
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Tencent-Hy4-preview (780B parameters), served in BF16 format with vLLM and speculative decoding on DGX B300 (TP=8), reached a generation speed of 65.7 tok/s per request and a time to first token (TTFT) of 110 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 65 users for chat use and 16 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.