DeepSeek-R1 vs NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
DeepSeek-R1 (684.5B parameters) and NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 (560.5B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | DeepSeek-R1 | NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 |
|---|---|---|
| Parameters | 684.5B | 560.5B (Mixture-of-experts: 22 of 512 experts active per token (~55B active parameters, per the model's own "A55B" name)) |
| Architecture | Multi-head latent attention; mixture of 256 experts, 8 active per token | mixture of 512 experts, 22 active per token |
| Context length | 163,840 tokens | 256K tokens (262,144) |
| License | – | NVIDIA OpenMDW-1.1 |
| Published precision | F8_E4M3 | BF16 |
| VRAM needed, As published | 765 GB | 1253 GB |
| VRAM needed, FP8 | Not a smaller option | 626 GB |
| VRAM needed, INT4 | 383 GB | 313 GB |
| Cheapest live fit, As published | RTX PRO 6000 × 8 · $11.75/hr | No live fit |
| Cheapest live fit, FP8 | – | RTX PRO 6000 × 7 · $9.63/hr |
| Cheapest live fit, INT4 | RTX A6000 × 8 · $2.90/hr | RTX A6000 × 7 · $2.54/hr |
| KV cache per token (16-bit) | 69 KB | Not published for this architecture |
| KV cache at 32k tokens | 2.14 GB | Not published for this architecture |
| KV cache at 128k tokens | 8.58 GB | Not published for this architecture |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- DeepSeek-R1 needs less VRAM at its published precision (765 GB against 1253 GB), so it fits on a smaller GPU.
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 lists the longer context window (262,144 tokens against 163,840).
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- DeepSeek-R1: full VRAM table and live GPU fit
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16: full VRAM table and live GPU fit
- The DeepSeek model series
- The Nemotron model series
Other comparisons
