SmolLM 135M — Hardware Requirements & GPU Compatibility
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SmolLM 135M is the original first-generation small language model from Hugging Face, designed to push the boundaries of what is achievable at extremely low parameter counts. With just 135 million parameters, it was a pioneering effort in making capable language models accessible on the most resource-constrained hardware. While the SmolLM2 and SmolLM3 families have since surpassed it in quality, the original SmolLM 135M remains a useful reference point for research and a practical option for ultra-lightweight deployment scenarios where every megabyte of memory counts.
175.5K downloads
253 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 135M
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-08-01
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM 135M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.4 GB | — | 0.06 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 0.4 GB | — | 0.06 GB | Importance-weighted 3-bit, small |
| IQ3_XS | 3.30 | 0.4 GB | — | 0.06 GB | Importance-weighted 3-bit, extra small |
| IQ3_M | 3.60 | 0.4 GB | — | 0.06 GB | Importance-weighted 3-bit, medium |
| Q3_K_S | 3.50 | 0.4 GB | — | 0.06 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.4 GB | — | 0.07 GB | 3-bit medium quantization |
| Q4_K_S | 4.50 | 0.4 GB | — | 0.08 GB | 4-bit small quantization |
| Q3_K_L | 4.10 | 0.4 GB | — | 0.07 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.4 GB | — | 0.07 GB | Importance-weighted 4-bit, compact |
| Q4_K_M | 4.80 | 0.4 GB | — | 0.08 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.4 GB | — | 0.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_S | 5.50 | 0.4 GB | — | 0.09 GB | 5-bit small quantization |
| Q6_K | 6.60 | 0.5 GB | — | 0.11 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.5 GB | — | 0.13 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM 135M?
Q4_K_M · 0.4 GBSmolLM 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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM 135M?
Q4_K_M · 0.4 GB33 devices with unified memory can run SmolLM 135M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does SmolLM 135M need?
SmolLM 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)
VRAM usage by quantization
Q4_K_M0.4 GB- What's the best quantization for SmolLM 135M?
For SmolLM 135M, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.4 GB.
VRAM requirement by quantization
Q2_K0.4 GB~75%IQ3_M0.4 GB~78%Q3_K_L0.4 GB~86%Q4_K_M ★0.4 GB~89%Q5_K_M0.4 GB~92%Q8_00.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM 135M on a Mac?
SmolLM 135M requires at least 0.4 GB at Q2_K, 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 SmolLM 135M locally?
Yes — SmolLM 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 SmolLM 135M?
At Q4_K_M, SmolLM 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 SmolLM 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 (Q2_K) is 0.06 GB.