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 | 1.3M | 4.3M |
| Last observed | 2026-10-16 | 2026-10-16 |
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
3.2B parameters in 6.4 GB of safetensors — the published 12.9 GB also carries an original/consolidated.00.pth copy, so you can shed half the download. Memory runs 7.7 GB at 16-bit, 3.9 at 8-bit, 1.9 at 4-bit; one MI300X at $1.85 an hour covers every precision, and at these sizes the card serves thousands of concurrent small chats. The economics are dominated by everything but the weights.
The license is llama3.2 and access is requested at the publisher — accept the terms before download; it is not a standard permissive grant. The page states no context length and no evaluations on our record. The natural neighbors are the fine-tunes listed nearby and Qwen's instruct models of similar size; pick Llama when your toolchain or compliance stack is already Meta-shaped, otherwise an Apache-2.0 alternative removes a legal review step.
SAVRN's Notes on Qwen2.5-3B-Instruct
The smallest Qwen2.5 instruct checkpoint still answers 4.1M downloads a month, and the run costs are pocket change: 7.4 GB at 16-bit, 3.7 GB at 8-bit, 1.9 GB at 4-bit, each fitting the cheapest Index card — one MI300X at $1.85 an hour — with room for dozens of concurrent copies. The 32,768-token window is generous for a 3.1B model, and it more than doubles what some 30B-class neighbors on this list carry.
The license field reads 'other', so read the publisher's license file before commercial deployment; nothing can be assumed from the label. The lineage points back to Qwen/Qwen2.5-3B for anyone planning their own tune, and the weights are 6.2 GB of safetensors across 12 files, unchanged since September 2024. Pick this for high-volume instruction following and JSON-shaped output per card; step to the Qwen2.5-Coder-7B below when the workload is code.
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.