SmolLM2 135M is a lightweight language model developed by HuggingFace, designed for efficient local deployment on devices with limited resources. With just 135 million parameters, this model offers a balance between performance and resource consumption, making it particularly suitable for text generation tasks that require quick responses without heavy computational overhead. It excels in generating coherent and contextually relevant text, thanks to its impressive context length of 8192 tokens, which allows it to maintain a broader understanding of the input text compared to many smaller models.
Despite its relatively small size, SmolLM2 135M holds its own against larger models in its class, demonstrating good efficiency and effectiveness. It punches above its weight in terms of text quality and coherence, making it a solid choice for applications where real-time performance and low resource usage are crucial. The model supports quantization options like Q8_0 and FP16, further enhancing its efficiency and reducing memory requirements. Users with devices equipped with as little as 0.6–0.8 GB of VRAM can comfortably run this model, making it an excellent option for developers, hobbyists, and businesses looking to deploy text generation capabilities on edge devices, laptops, or other resource-constrained environments.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8 | 0.135 GB | 0.64 GB | 1.14 GB | 98% |
| FP16 | 16 | 0.252 GB | 0.75 GB | 1.25 GB | 100% |
Context window & KV cache
Adds 0.13 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 8K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run SmolLM2 135M
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull smollm2:135m - 2
Chat
ollama run smollm2:135m - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"smollm2:135m","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running SmolLM2 135M on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host SmolLM2 135Mfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
1.2 GB
0.6 GB weights + 0.1 GB KV
Aggregate tok/s
1852
across 1 user
Per-user tok/s
1852
0.135 B dense
✅ Fits in 24 GB VRAM with 22.8 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run SmolLM2 135M?
SmolLM2 135M requires 0.64 GB VRAM minimum with Q8_0 quantization. For full precision you need 0.75 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run SmolLM2 135M?
SmolLM2 135M requires at least 0.6 GB to 0.8 GB of VRAM, depending on the quantization level. It can run on most modern GPUs, including those in laptops and smartphones.
Is SmolLM2 135M good for coding?
SmolLM2 135M is suitable for basic coding tasks and quick experiments due to its small size and fast inference times, but it may not handle complex or specialized coding scenarios as well as larger models.
SmolLM2 135M vs Llama 3.1 8B?
SmolLM2 135M has significantly fewer parameters (135M vs 8B), making it much lighter and faster but less capable in terms of language understanding and generation compared to Llama 3.1 8B.
Can I run SmolLM2 135M on a Mac?
Yes, SmolLM2 135M can run on a Mac, including both Intel and M1/M2 chips, as it has low hardware requirements and is optimized for efficiency.
How much VRAM does SmolLM2 135M need?
SmolLM2 135M requires between 0.6 GB and 0.8 GB of VRAM, depending on the quantization level used during inference.
Is SmolLM2 135M censored?
SmolLM2 135M is not explicitly censored, but it adheres to community guidelines and ethical standards typical of open-source models.
Is SmolLM2 135M commercial-use allowed?
Yes, SmolLM2 135M is licensed under Apache-2.0, which allows for commercial use, provided you comply with the license terms.
SmolLM2 135M context length?
SmolLM2 135M supports a context length of 8192 tokens, allowing for longer inputs and outputs compared to many smaller models.
Does SmolLM2 135M support function calling?
SmolLM2 135M does not natively support function calling, but you can implement custom logic to handle function calls in your application.
SmolLM2 135M quantization options?
SmolLM2 135M supports various quantization levels, typically 8-bit and 4-bit, which reduce the model size and VRAM usage while maintaining reasonable performance.
Can SmolLM2 135M run on CPU?
Yes, SmolLM2 135M can run on CPU, although it will be slower than on GPU. It is designed to be lightweight and efficient, making it suitable for CPU inference.
SmolLM2 135M fine-tuning?
SmolLM2 135M can be fine-tuned using frameworks like Hugging Face Transformers. Fine-tuning can improve its performance on specific tasks but may require additional computational resources.
SmolLM2 135M system requirements?
SmolLM2 135M requires at least 0.6 GB to 0.8 GB of VRAM, 2 GB of RAM, and a modern CPU. It is compatible with most devices, including smartphones and laptops.
SmolLM2 135M performance benchmark?
SmolLM2 135M processes around 100-200 tokens per second on a mid-range GPU, making it suitable for real-time applications and quick experiments.
SmolLM2 135M for RAG?
SmolLM2 135M can be used for Retrieval-Augmented Generation (RAG) tasks, but its smaller size may limit its effectiveness compared to larger models in handling complex retrieval and generation tasks.
SmolLM2 135M for agents?
SmolLM2 135M is suitable for creating lightweight conversational agents and chatbots, especially when resource constraints are a concern.
SmolLM2 135M for coding vs general?
SmolLM2 135M performs reasonably well for both coding and general text generation tasks, but it may not excel in highly specialized coding scenarios compared to models trained specifically for programming.
SmolLM2 135M vs ChatGPT?
SmolLM2 135M is much smaller (135M vs billions of parameters) and more lightweight, making it easier to run locally, but it offers less advanced language capabilities compared to ChatGPT.
SmolLM2 135M download size?
The download size of SmolLM2 135M is approximately 145 MB, making it easy to download and deploy on a variety of devices.
Best quant for SmolLM2 135M?
For optimal balance between performance and resource usage, 8-bit quantization is recommended for SmolLM2 135M, reducing VRAM usage while maintaining good accuracy.