SAVRN Model Hub · Comparisons
gpt2-large vs Qwen3-0.6B
Gpt2-large has 812M parameters and Qwen3-0.6B has 752M parameters; gpt2-large is released under MIT License and Qwen3-0.6B under Apache License 2.0; at 16-bit, gpt2-large needs about 1.9 GB (1x MI300X from $1.85 an hour) and Qwen3-0.6B about 1.8 GB (1x MI300X from $1.85 an hour).
| Field | gpt2-large openai-community/gpt2-large | Qwen3-0.6B Qwen/Qwen3-0.6B |
|---|---|---|
| Publisher | OpenAI community | Qwen |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 812M parameters | 752M parameters |
| Architecture | GPT2LMHeadModel | Qwen3ForCausalLM |
| Library | transformers | transformers |
| Context length | Not stated | 40,960 tokens |
| Repository size | 26.3 GB | 1.5 GB |
| Artifact formats | safetensors, onnx, pytorch, jax, rust, tf | safetensors |
| License | mit | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 1.9 GB | 1.8 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.5 GB | 0.5 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 32b71b12589c | c1899de289a0 |
| Downloads reported by the hub | 1M | 30.6M |
| Last observed | 2026-10-13 | 2026-10-13 |
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 gpt2-large
Published March 2, 2022, this English causal language model moves 1,243,231 downloads a month. The configuration counts 812M parameters; the publisher says 774M. At 16-bit the weights are 1.6 GB and need 1.9 GB, 8-bit needs 1.0 GB and 4-bit 0.5 GB, so the cheapest host in our data, one MI300X with 192 GB at $1.85 per hour, is far more card than it needs. Prove a pipeline on it, then move the real workload in.
MIT is a short permissive license: keep the copyright and permission notice and you can use, modify and redistribute it commercially. Our facts record no context length for it, so confirm the window in the configuration you load. The repository totals 26.3 GB across 29 files because it ships six formats, and the one paper it cites, arXiv:1910.09700, covers the carbon emissions of machine learning, not this model.
SAVRN's Notes on Qwen3-0.6B
752M parameters paired with a 40,960-token context is the story here: a sub-billion-parameter model that reads whole documents. Memory runs 1.8 GB at 16-bit and 0.5 GB at 4-bit, so the cheapest setup we list — one 192 GB MI300X at $1.85 an hour — would hold more than a hundred copies; it rides beside larger workloads. At 29.6M downloads a month it leads every text-generation model in this part of the directory.
Apache 2.0 clears commercial use, modification and redistribution, notices kept and significant changes stated. The weights are one 1.5 GB safetensors file in bfloat16 over 28 layers. It derives from Qwen3-0.6B-Base, and the Qwen3 technical report (arXiv:2505.09388) sets out the design — start from the base if you plan your own post-training. No host on our Index quotes a per-token price, so the hourly card is the only cost signal.
Questions
Which is larger, gpt2-large or Qwen3-0.6B?
gpt2-large (812M parameters) is larger than Qwen3-0.6B (752M parameters), by the parameter counts their publishers report.
Which is cheaper to run, gpt2-large or Qwen3-0.6B?
At 4-bit, gpt2-large fits on 1x MI300X from $1.85 an hour and Qwen3-0.6B on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use gpt2-large commercially?
Yes. gpt2-large is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
Can I use Qwen3-0.6B commercially?
Yes. Qwen3-0.6B 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.