SmolLM2 135M vs SmolLM2 360M
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | SmolLM2 135M | SmolLM2 360M |
|---|---|---|
| Parameters | 0.135B | 0.36B |
| Architecture | smollm | smollm |
| License | Apache 2.0 | Apache 2.0 |
| Context Length | 8K tokens | 8K tokens |
| Category | Language Model | Language Model |
| Author | HuggingFace | HuggingFace |
| HF Downloads | 1.3M | 286.8K |
| VRAM Range | 0.64 - 0.75 GB | 0.75 - 0.86 GB |
| Quantizations | 2 options | 2 options |
| Best Quality Score | 100% | 98% |
Quantization Options
SmolLM2 135M
SmolLM2 360M
In-depth comparison
SmolLM2 135M is the better choice for most users due to its smaller size and lower VRAM requirements, making it ideal for devices with limited resources. However, SmolLM2 360M offers slightly better quality for more demanding tasks.
When to choose SmolLM2 135M
SmolLM2 135M is the better pick for users with highly constrained devices, such as older smartphones or low-end computers. Its minimal VRAM requirement of 0.6GB ensures it runs smoothly even on devices with limited memory. Additionally, its small 145MB download size makes it perfect for quick experiments and deployments where storage space is at a premium.
When to choose SmolLM2 360M
SmolLM2 360M is the better choice for users who need a bit more quality and coherence in their text generation tasks, especially on devices that can handle a slightly higher VRAM requirement of 0.8GB. This model is ideal for applications like chatbots, content creation, and summarization where the additional parameters can provide more nuanced and contextually relevant outputs.
Quality
While SmolLM2 135M has a best quality score of 100%, SmolLM2 360M, with 98%, is still very competitive and may produce more coherent and contextually relevant text due to its larger parameter count. The slight drop in quality is offset by the improved performance in more complex tasks.
Performance & hardware fit
SmolLM2 135M requires only 0.6GB of VRAM, making it highly suitable for devices with limited memory. In contrast, SmolLM2 360M requires 0.8GB of VRAM, which is still quite low but might be a consideration for the most resource-constrained devices. SmolLM2 135M is also likely to be faster due to its smaller size, though the difference may not be significant for most users.
Use-case fit
| coding | SmolLM2 135M | SmolLM2 135M's smaller size and faster performance make it ideal for coding tasks where quick feedback is essential. |
| creative writing | SmolLM2 360M | SmolLM2 360M's slightly better quality and coherence make it more suitable for creative writing tasks that require nuanced and contextually rich text. |
| RAG / retrieval | SmolLM2 360M | For RAG and retrieval tasks, the additional parameters in SmolLM2 360M can help generate more accurate and contextually relevant responses. |
| agent / tool use | SmolLM2 135M | SmolLM2 135M's efficiency and low resource requirements make it ideal for agent and tool use, especially on devices with limited computational power. |
| running on consumer GPU (8-12GB) | Tie | Both models will run efficiently on consumer GPUs with 8-12GB of VRAM, so the choice depends on the specific needs of the user. |
| long context (16K+) | Tie | Both models support a context length of 8192 tokens, so neither is better suited for long context tasks beyond this limit. |
SmolLM2 135M wins for most users due to its efficiency and low resource requirements, making it ideal for a wide range of devices. SmolLM2 360M is the better choice for users who prioritize slightly better output quality and can afford the extra VRAM.