SAVRN Model Hub · Comparisons
pythia-160m vs SmolLM2-135M
Pythia-160m has 213M parameters and SmolLM2-135M has 135M parameters; both are released under Apache License 2.0; at 16-bit, pythia-160m needs about 0.5 GB (1x MI300X from $1.85 an hour) and SmolLM2-135M about 0.3 GB (1x MI300X from $1.85 an hour).
| Field | pythia-160m EleutherAI/pythia-160m | SmolLM2-135M HuggingFaceTB/SmolLM2-135M |
|---|---|---|
| Publisher | EleutherAI | Hugging Face Smol Models Research |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 213M parameters | 135M parameters |
| Architecture | GPTNeoXForCausalLM | LlamaForCausalLM |
| Library | transformers | transformers |
| Context length | 2,048 tokens | 8,192 tokens |
| Repository size | 752.2 MB | 272.4 MB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | apache-2.0 | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.5 GB | 0.3 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.1 GB | 0.1 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 50f5173d932e | 93efa2f097d5 |
| Downloads reported by the hub | 3.5M | 2.2M |
| Last observed | 2026-09-19 | 2026-09-19 |
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.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
SmolLM2-135M
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| TIGER-Lab/MMLU-Pro | Task mmlu_proMetric mmlu_proComparison conditions not established | 10.85 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
SAVRN's Notes on pythia-160m
Nobody stands up an accelerator for a model that fits in half a gigabyte, which is the point of Pythia-160m. EleutherAI built the Pythia Scaling Suite for interpretability research: eight sizes from 70M to 12B, trained on the Pile in the same order, with 154 intermediate checkpoints each. It needs 0.5 GB at 16-bit; 8-bit takes that to 0.3 GB and 4-bit to 0.1 GB. The price table bottoms out at one MI300X with 192 GB at $1.85 an hour on-demand, a floor rather than a fit. We would run it in spare memory.
Nothing on the license side slows you down: Apache 2.0 permits commercial use, modification and redistribution if you keep the license and copyright notices and state significant changes. Two checks before building on it: the 2,048 token context, and the parameter count, 213M on the listing against the 160m in the name.
SAVRN's Notes on SmolLM2-135M
Two trillion training tokens went into a model that fits in 0.3 GB. Hugging Face Smol Models Research trained this 135M-parameter SmolLM2, smallest of three sizes beside 360M and 1.7B, on FineWeb-Edu, DCLM and The Stack to run on-device. The cheapest priced card we list is one MI300X at $1.85 an hour, and 0.3 GB needed at 16-bit is why we would never dedicate one; here it shares a card, or goes to the device it was built for.
The only evaluation in our file is a third-party MMLU-Pro result of 10.85 from EvalEval, so run your own task first, and plan around an 8,192-token context; the training recipe is in arXiv:2502.02737. Apache 2.0 permits commercial use, modification and redistribution, requires keeping the license, copyright notices and any NOTICE file and stating significant changes, and carries an express patent grant from contributors, which matters if you fine-tune and ship.
Questions
Which is larger, pythia-160m or SmolLM2-135M?
pythia-160m (213M parameters) is larger than SmolLM2-135M (135M parameters), by the parameter counts their publishers report.
Which is cheaper to run, pythia-160m or SmolLM2-135M?
At 4-bit, pythia-160m fits on 1x MI300X from $1.85 an hour and SmolLM2-135M on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use pythia-160m commercially?
Yes. pythia-160m 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.
Can I use SmolLM2-135M commercially?
Yes. SmolLM2-135M 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.