NVIDIA-Nemotron-3-Nano-4B-BF16 vs Llama-3.2-3B-Instruct-FP8-dynamic
Specs, VRAM requirements and download trends — updated 21 September 2026.
RedHatAI · B
Params
3.6 B
Context
131,072
30d
277 K
llama3.2 licence · conditional
Specification comparison
Differences are highlighted; identical values are muted.
| Specification | NVIDIA-Nemotron-3-Nano-4B-BF16 | Llama-3.2-3B-Instruct-FP8-dynamic |
|---|---|---|
| Parameters | 4.0 B | 3.6 B |
| Architecture | NemotronHForCausalLM | LlamaForCausalLM |
| Context length | 262,144 | 131,072 |
| Licence | other | llama3.2 |
| Commercial use | Unknown | Conditional |
| Languages | 1 | 8 |
| Downloads 30d | 3,492,652 | 276,876 |
| Downloads all time | 6.9 M | 358 K |
| Quantizations on the Hub | SAFETENSORS | SAFETENSORS |
| Gated | No | No |
| First seen on the Hub | 2026-03-07 | 2024-09-25 |
Download trend
Daily snapshots, last 56 days (28 Jul – 21 Sep)
3.5 M207 K
NVIDIA-Nemotron-3-Nano-4B-BF16
Llama-3.2-3B-Instruct-FP8-dynamic
VRAM side by side
On RTX 4090 · 24 GB · 8K context unless noted
Llama-3.2-3B-Instruct-FP8-dynamic · fp16 @ 128K ctx
14.0 GB / 24 GB
NVIDIA-Nemotron-3-Nano-4B-BF16 · fp16 @ 256K ctx
28.3 GB / 24 GB
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Qwen3-4B-Instruct-2507 vs NVIDIA-Nemotron-3-Nano-4B-BF16
Qwen2.5-3B-Instruct vs NVIDIA-Nemotron-3-Nano-4B-BF16
Llama-3.1-8B-Instruct vs NVIDIA-Nemotron-3-Nano-4B-BF16