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 |
| 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_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 |
| 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 GBIQ3_M0.4 GBQ3_K_L0.4 GBQ4_K_M ★0.4 GBQ5_K_M0.4 GBQ8_00.5 GB★ 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)
~6779 tok/s~1524 tok/s~5067 tok/s~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.
- Which GPUs can run SmolLM 135M?
35 consumer GPUs can run SmolLM 135M at Q4_K_M (0.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run SmolLM 135M?
33 devices with unified memory can run SmolLM 135M at Q4_K_M (0.4 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.