SAVRN Model Hub · Comparisons
NVIDIA-Nemotron-3-Nano-4B-BF16 vs Qwen3-4B
NVIDIA-Nemotron-3-Nano-4B-BF16 has 4B parameters and Qwen3-4B has 4B parameters; NVIDIA-Nemotron-3-Nano-4B-BF16 is released under other and Qwen3-4B under Apache License 2.0; at 16-bit, NVIDIA-Nemotron-3-Nano-4B-BF16 needs about 9.5 GB (1x MI300X from $1.85 an hour) and Qwen3-4B about 9.7 GB (1x MI300X from $1.85 an hour).
| Field | NVIDIA-Nemotron-3-Nano-4B-BF16 nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 | Qwen3-4B Qwen/Qwen3-4B |
|---|---|---|
| Publisher | NVIDIA | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 4B parameters | 4B parameters |
| Architecture | NemotronHForCausalLM | Qwen3ForCausalLM |
| Library | transformers | transformers |
| Context length | 262,144 tokens | 40,960 tokens |
| Repository size | 8.0 GB | 8.1 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | other | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 9.5 GB | 9.7 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 | 2.4 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | dfaf35de3e30 | 1cfa9a720891 |
| Downloads reported by the hub | 3.5M | 7.2M |
| 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 Qwen3-4B
Memory is not the constraint with Qwen3-4B. The 16-bit weights are 8.0 GB and need 9.7 GB to serve, 8-bit needs 4.8 GB and 4-bit 2.4 GB, so on the cheapest Index setup, one MI300X with 192 GB at $1.85 an hour, the card sits mostly empty and the decision is how many copies to run. It is text only, switches between a thinking mode and a plain dialogue mode, and carries a 40,960-token context.
Apache 2.0 lets you fine-tune it, ship it in a product and charge for it, provided the license and notices travel with it and you state what you changed. Before you commit, look at the lineage and the price sheet: it is derived from Qwen3-4B-Base, so decide whether you want this release or the base for post-training, and the page lists no Index host prices, so there is nothing per token to compare with.
Questions
Which is larger, NVIDIA-Nemotron-3-Nano-4B-BF16 or Qwen3-4B?
Qwen3-4B (4B parameters) is larger than NVIDIA-Nemotron-3-Nano-4B-BF16 (4B parameters), by the parameter counts their publishers report.
Which is cheaper to run, NVIDIA-Nemotron-3-Nano-4B-BF16 or Qwen3-4B?
At 4-bit, NVIDIA-Nemotron-3-Nano-4B-BF16 fits on 1x MI300X from $1.85 an hour and Qwen3-4B on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Qwen3-4B commercially?
Yes. Qwen3-4B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.