SAVRN Model Hub · Comparisons
NVIDIA-Nemotron-3-Nano-4B-BF16 vs Qwen2.5-3B-Instruct
NVIDIA-Nemotron-3-Nano-4B-BF16 has 4B parameters and Qwen2.5-3B-Instruct has 3.1B parameters; both are released under other; at 16-bit, NVIDIA-Nemotron-3-Nano-4B-BF16 needs about 9.5 GB (1x MI300X from $1.85 an hour) and Qwen2.5-3B-Instruct about 7.4 GB (1x MI300X from $1.85 an hour).
| Field | NVIDIA-Nemotron-3-Nano-4B-BF16 nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 | Qwen2.5-3B-Instruct Qwen/Qwen2.5-3B-Instruct |
|---|---|---|
| Publisher | NVIDIA | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 4B parameters | 3.1B parameters |
| Architecture | NemotronHForCausalLM | Qwen2ForCausalLM |
| Library | transformers | transformers |
| Context length | 262,144 tokens | 32,768 tokens |
| Repository size | 8.0 GB | 6.2 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | other | other |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 9.5 GB | 7.4 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 2.4 GB | 1.9 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | dfaf35de3e30 | aa8e72537993 |
| Downloads reported by the hub | 3.5M | 5.1M |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
SAVRN's Notes on NVIDIA-Nemotron-3-Nano-4B-BF16
Where in the rack does a 4 billion parameter reasoning model belong? At 16-bit it runs in 9.5 GB, at 8-bit in 4.8 GB, at 4-bit in 2.4 GB. The Index's cheapest fit is a single MI300X at $1.85 an hour on-demand, and its 192 GB is more than one copy needs. NVIDIA trained it from scratch for reasoning and non-reasoning work: it writes a reasoning trace before the final answer, and a system prompt switches the trace off, which sets how many output tokens each request burns.
The license field reads other, with no summary on our side, so the terms come from NVIDIA, not a standard license; read them before production. Measure your longest inputs against the 262,144 token window, note the September 2024 pretraining cutoff and the lineage: derived from NVIDIA-Nemotron-Nano-9B-v2, trained on seven named NVIDIA sets, Nemotron-CC-v2 to Nemotron-Math-Proofs-v1. No per-token host prices are listed.
SAVRN's Notes on Qwen2.5-3B-Instruct
At 16-bit, 7.4 GB is all this one asks for. Qwen2.5-3B-Instruct carries 3.1 billion parameters and a 32,768-token window, aimed at instruction following, structured data, JSON output and long text past 8K tokens. The cheapest Index setup is one MI300X with 192 GB at $1.85 an hour; 8-bit needs 3.7 GB and 4-bit 1.9 GB. Nobody sizes a 192 GB card for a 7.4 GB model, so it shares the card, beside a larger model or as several copies.
The weights are open access, so nothing gates the files, but the license field reads 'other' and carries no summary; the terms live in the publisher's license file, and nothing about commercial use can be assumed until that file has been read. Then check the lineage: this build derives from the Qwen2.5-3B base, where any fine-tune would start, and its paper is the Qwen2 technical report, arXiv:2407.10671.
Questions
Which is larger, NVIDIA-Nemotron-3-Nano-4B-BF16 or Qwen2.5-3B-Instruct?
NVIDIA-Nemotron-3-Nano-4B-BF16 (4B parameters) is larger than Qwen2.5-3B-Instruct (3.1B parameters), by the parameter counts their publishers report.
Which is cheaper to run, NVIDIA-Nemotron-3-Nano-4B-BF16 or Qwen2.5-3B-Instruct?
At 4-bit, NVIDIA-Nemotron-3-Nano-4B-BF16 fits on 1x MI300X from $1.85 an hour and Qwen2.5-3B-Instruct on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.