NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs Qwen2.5-32B-Instruct
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B parameters) and Qwen2.5-32B-Instruct (32.8B 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 | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | Qwen2.5-32B-Instruct |
|---|---|---|
| Parameters | 31.6B | 32.8B |
| Architecture | Hybrid (some layers use full attention); mixture of 128 experts, 6 active per token | Grouped-query attention |
| Context length | 262,144 tokens | 32,768 tokens |
| License | – | – |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 70.6 GB | 73.2 GB |
| VRAM needed, FP8 | 35.3 GB | 36.6 GB |
| VRAM needed, INT4 | 17.6 GB | 18.3 GB |
| Cheapest live fit, As published | A100 · $1.21/hr | A100 · $1.21/hr |
| Cheapest live fit, FP8 | L40 · $0.742/hr | L40 · $0.742/hr |
| Cheapest live fit, INT4 | RTX A5000 · $0.176/hr | RTX A5000 · $0.176/hr |
| KV cache per token (16-bit) | 6 KB | 256 KB |
| KV cache at 32k tokens | 0.19 GB | 8.00 GB |
| KV cache at 128k tokens | 0.75 GB | 32.0 GB |
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
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 needs less VRAM at its published precision (70.6 GB against 73.2 GB), so it fits on a smaller GPU.
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 lists the longer context window (262,144 tokens against 32,768).
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 caches less per sequence at 32k tokens (0.2 GB against 8.0 GB), leaving more memory for batching.
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
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16: full VRAM table and live GPU fit
- Qwen2.5-32B-Instruct: full VRAM table and live GPU fit
- The Nemotron model series
- The Qwen model series
- All models that fit in 80 GB
Other comparisons
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs Qwen3-32B
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs Qwen3-30B-A3B
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs GLM-4.7-Flash
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs Qwen2.5-Coder-32B-Instruct
- Qwen2.5-32B-Instruct vs GLM-4.7-Flash
- Qwen2.5-32B-Instruct vs NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
- Qwen2.5-32B-Instruct vs granite-4.1-30b
