LLM Inference Benchmark Explorer
Ripple-65B-Preview on DGX B300 — NVFP4, SGLang, TP1, speculative decoding k=16 inference benchmark
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| Model | Ripple-65B-Preview |
|---|---|
| Parameters | 65B |
| Intelligence Index | — |
| Agentic Index | — |
| Device | DGX B300 |
| Quantization | NVFP4 |
| Max C | 128 |
| Chat Capacity | 318 |
| Agentic Capacity | 93 |
| TP | — |
| DP | — |
| PP | — |
| Engine | SGLang |
| Speculative Decoding | Yes |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 52 | 53.57 | PASS |
| 2 | 68 | 51.30 | PASS |
| 4 | 81 | 50.21 | PASS |
| 8 | 118 | 48.55 | PASS |
| 16 | 141 | 46.56 | PASS |
| 32 | 220 | 43.59 | PASS |
| 64 | 357 | 39.09 | PASS |
| 128 | 638 | 30.63 | PASS |
| 256 | 1324 | 18.05 | FAIL |
In OpenZeka's measurement, Ripple-65B-Preview (65B parameters), served in NVFP4 format with SGLang and speculative decoding on DGX B300, reached a generation speed of 53.6 tok/s per request and a time to first token (TTFT) of 52 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 318 users for chat use and 93 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: DFlash2 k=16, 2M context. Served as ripple-65b; NVFP4 weights, size estimated. Model: ricursiveAI/Ripple-65B-Preview-NVFP4
Intelligence Index and Agentic Index values are published by Artificial Analysis and are reproduced here with attribution.
In OpenZeka's measurement, Ripple-65B-Preview (65B parameters), served in NVFP4 format with SGLang and speculative decoding on DGX B300, reached a generation speed of 53.6 tok/s per request and a time to first token (TTFT) of 52 ms with a single request.
Considering the speed targets and the available KV cache capacity, the estimated capacity is 318 users for chat use and 93 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.