SmolLM2 135M — Hardware Requirements & GPU Compatibility
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SmolLM2 135M is one of the smallest capable language models available, developed by Hugging Face as part of their SmolLM2 family. With just 135 million parameters, it requires virtually no VRAM and can run on almost any hardware, making it an excellent starting point for researchers experimenting with language model behavior, fine-tuning workflows, or edge deployment scenarios. Despite its tiny footprint, SmolLM2 135M benefits from improved training data and techniques compared to its first-generation predecessor. It is best suited for lightweight text generation tasks, prototyping, and educational purposes rather than production-grade applications.
817.7K downloads
171 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 135M
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2025-02-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM2 135M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_L | 4.10 | 0.4 GB | 0.6 GB | 0.07 GB | 3-bit large quantization |
| Q4_K_M | 4.80 | 0.4 GB | 0.6 GB | 0.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 0.5 GB | 0.6 GB | 0.11 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.5 GB | 0.6 GB | 0.13 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM2 135M?
Q4_K_M · 0.4 GBSmolLM2 135M (Q4_K_M) requires 0.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 8K context window can add up to 0.1 GB, bringing total usage to 0.6 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM2 135M?
Q4_K_M · 0.4 GB33 devices with unified memory can run SmolLM2 135M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does SmolLM2 135M need?
SmolLM2 135M requires 0.4 GB of VRAM at Q4_K_M, or 0.5 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 135M × 4.8 bits ÷ 8 = 0.1 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.5 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M0.4 GBQ4_K_M + full context0.6 GB- What's the best quantization for SmolLM2 135M?
For SmolLM2 135M, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q6_K (0.5 GB) provides better quality if you have the VRAM. The smallest option is Q3_K_L at 0.4 GB.
VRAM requirement by quantization
Q3_K_L0.4 GB~86%Q4_K_M ★0.4 GB~89%Q6_K0.5 GB~95%Q8_00.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 135M on a Mac?
SmolLM2 135M requires at least 0.4 GB at Q3_K_L, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run SmolLM2 135M locally?
Yes — SmolLM2 135M can run locally on consumer hardware. At Q4_K_M quantization it needs 0.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM2 135M?
At Q4_K_M, SmolLM2 135M can reach ~6779 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1524 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 0.4 × 0.55 = ~6779 tok/s
Estimated speed at Q4_K_M (0.4 GB)
AMD Instinct MI300X~6779 tok/sNVIDIA GeForce RTX 4090~1524 tok/sNVIDIA H100 SXM~5067 tok/sAMD Instinct MI250X~4191 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM2 135M?
At Q4_K_M, the download is about 0.08 GB. The full-precision Q8_0 version is 0.13 GB. The smallest option (Q3_K_L) is 0.07 GB.