SAVRN Model Hub · Comparisons
Llama-3.2-3B-Instruct vs Qwen3-4B
Llama-3.2-3B-Instruct has 3.2B parameters and Qwen3-4B has 4B parameters; Llama-3.2-3B-Instruct is released under llama3.2 and Qwen3-4B under Apache License 2.0; at 16-bit, Llama-3.2-3B-Instruct needs about 7.7 GB (1x MI300X from $1.85 an hour) and Qwen3-4B about 9.7 GB (1x MI300X from $1.85 an hour).
| Field | Llama-3.2-3B-Instruct meta-llama/Llama-3.2-3B-Instruct | Qwen3-4B Qwen/Qwen3-4B |
|---|---|---|
| Publisher | Meta Llama | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 3.2B parameters | 4B parameters |
| Architecture | LlamaForCausalLM | Qwen3ForCausalLM |
| Library | transformers | transformers |
| Context length | Not stated | 40,960 tokens |
| Repository size | 12.9 GB | 8.1 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | llama3.2 | apache-2.0 |
| Access | Access requested at publisher | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 7.7 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) | 1.9 GB | 2.4 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 0cb88a4f764b | 1cfa9a720891 |
| Downloads reported by the hub | 1.9M | 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 Llama-3.2-3B-Instruct
Memory is the easy part with this one. The 16-bit weights are 6.4 GB and it needs 7.7 GB to run, 3.9 GB at 8-bit, 1.9 GB at 4-bit, and our cheapest listed setup, one MI300X with 192 GB at $1.85 an hour on-demand, leaves most of the card free. That is the reason to pick 3.2 billion parameters tuned for multilingual dialogue, retrieval and summarization by agents: room for batches and other models.
The hard part is paperwork. Access is gated, so the publisher approves you first, and the license is listed as llama3.2 with no summary on record, so read the full terms before deploying commercially. Two more checks: the context length is missing from our record, and the repository is 12.9 GB across 16 files in both safetensors and pytorch formats against 6.4 GB of 16-bit weights, so fetch only the format you run.
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, Llama-3.2-3B-Instruct or Qwen3-4B?
Qwen3-4B (4B parameters) is larger than Llama-3.2-3B-Instruct (3.2B parameters), by the parameter counts their publishers report.
Which is cheaper to run, Llama-3.2-3B-Instruct or Qwen3-4B?
At 4-bit, Llama-3.2-3B-Instruct 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.