SAVRN Model Hub · Comparisons
Llama-3.2-3B-Instruct vs Qwen2.5-3B-Instruct
Llama-3.2-3B-Instruct has 3.2B parameters and Qwen2.5-3B-Instruct has 3.1B parameters; Llama-3.2-3B-Instruct is released under llama3.2 and Qwen2.5-3B-Instruct under other; at 16-bit, Llama-3.2-3B-Instruct needs about 7.7 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 | Llama-3.2-3B-Instruct meta-llama/Llama-3.2-3B-Instruct | Qwen2.5-3B-Instruct Qwen/Qwen2.5-3B-Instruct |
|---|---|---|
| Publisher | Meta Llama | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 3.2B parameters | 3.1B parameters |
| Architecture | LlamaForCausalLM | Qwen2ForCausalLM |
| Library | transformers | transformers |
| Context length | Not stated | 32,768 tokens |
| Repository size | 12.9 GB | 6.2 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | llama3.2 | other |
| Access | Access requested at publisher | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 7.7 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) | 1.9 GB | 1.9 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 0cb88a4f764b | aa8e72537993 |
| Downloads reported by the hub | 1.9M | 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 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 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, Llama-3.2-3B-Instruct or Qwen2.5-3B-Instruct?
Llama-3.2-3B-Instruct (3.2B parameters) is larger than Qwen2.5-3B-Instruct (3.1B parameters), by the parameter counts their publishers report.
Which is cheaper to run, Llama-3.2-3B-Instruct or Qwen2.5-3B-Instruct?
At 4-bit, Llama-3.2-3B-Instruct 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.