LLM Inference Benchmark Explorer
Nemotron-3-Ultra on DGX B300 — BF16, vLLM, TP8 inference benchmark
Open this configuration in the LLM Inference Benchmark Explorer
| Model | Nemotron-3-Ultra |
|---|---|
| Parameters | 550B |
| Intelligence Index | 22.9 |
| Agentic Index | 20.1 |
| Device | DGX B300 |
| Quantization | BF16 |
| Max C | 16 |
| Chat Capacity | 64 |
| Agentic Capacity | 24 |
| TP | 8 |
| DP | — |
| PP | — |
| Engine | vLLM |
| Speculative Decoding | — |
| C | TTFT (ms) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 132 | 177.32 | PASS |
| 2 | 465 | 128.50 | PASS |
| 4 | 134 | 118.36 | PASS |
| 8 | 159 | 97.07 | PASS |
| 16 | 266 | 59.35 | PASS |
| 32 | 2159 | 33.09 | FAIL |
| 64 | 6064 | 17.61 | FAIL |
In OpenZeka's measurement, Nemotron-3-Ultra (550B parameters), served in BF16 format with vLLM on DGX B300 (TP=8), reached a generation speed of 177.3 tok/s per request and a time to first token (TTFT) of 132 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, Nemotron-3-Ultra (550B parameters), served in BF16 format with vLLM on DGX B300 (TP=8), reached a generation speed of 177.3 tok/s per request and a time to first token (TTFT) of 132 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.