SmolLM3 3B — Hardware Requirements & GPU Compatibility
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SmolLM3 3B is Hugging Face's latest-generation compact language model, representing a significant step up from the SmolLM2 series. At 3 billion parameters, it delivers considerably stronger reasoning, instruction following, and general language understanding while maintaining modest hardware requirements that keep it accessible on most consumer GPUs. This model benefits from improved training data, architectural refinements, and lessons learned from previous SmolLM generations. It is well positioned for local chatbot applications, coding assistance, and content generation tasks where you want strong performance without dedicating the resources required by 7B-class models.
167.8K downloads
907 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 3B
- Architecture
- SmolLM3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-09-10
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does SmolLM3 3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 1.3 GB | 6.0 GB | 0.83 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 1.5 GB | 6.1 GB | 1.01 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 1.6 GB | 6.3 GB | 1.16 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 1.7 GB | 6.4 GB | 1.24 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 1.7 GB | 6.4 GB | 1.27 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.8 GB | 6.4 GB | 1.31 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 1.8 GB | 6.5 GB | 1.35 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 1.9 GB | 6.6 GB | 1.46 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.9 GB | 6.6 GB | 1.50 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 2.0 GB | 6.7 GB | 1.54 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 2.1 GB | 6.7 GB | 1.61 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 2.1 GB | 6.8 GB | 1.69 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 2.1 GB | 6.8 GB | 1.69 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 2.1 GB | 6.8 GB | 1.69 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 2.3 GB | 6.9 GB | 1.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 2.3 GB | 7.0 GB | 1.84 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 2.5 GB | 7.2 GB | 2.06 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 2.6 GB | 7.3 GB | 2.14 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 2.6 GB | 7.3 GB | 2.17 GB | 5-bit large quantization |
| Q6_K | 6.60 | 2.9 GB | 7.6 GB | 2.48 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | 8.1 GB | 3.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM3 3B?
Q4_K_M · 2.3 GBSmolLM3 3B (Q4_K_M) requires 2.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 66K context window can add up to 4.7 GB, bringing total usage to 6.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM3 3B?
Q4_K_M · 2.3 GB33 devices with unified memory can run SmolLM3 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (8)
Frequently Asked Questions
- How much VRAM does SmolLM3 3B need?
SmolLM3 3B requires 2.3 GB of VRAM at Q4_K_M, or 3.5 GB at Q8_0. Full 66K context adds up to 4.7 GB (6.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.1 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M2.3 GBQ4_K_M + full context6.9 GB- What's the best quantization for SmolLM3 3B?
For SmolLM3 3B, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.3 GB.
VRAM requirement by quantization
IQ2_XXS1.3 GB~53%Q3_K_S1.8 GB~77%IQ4_XS2.1 GB~87%Q4_K_M ★2.3 GB~89%Q4_K_L2.3 GB~90%Q8_03.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM3 3B on a Mac?
SmolLM3 3B requires at least 1.3 GB at IQ2_XXS, 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 SmolLM3 3B locally?
Yes — SmolLM3 3B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM3 3B?
At Q4_K_M, SmolLM3 3B can reach ~1296 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~291 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 ÷ 2.3 × 0.55 = ~1296 tok/s
Estimated speed at Q4_K_M (2.3 GB)
AMD Instinct MI300X~1296 tok/sNVIDIA GeForce RTX 4090~291 tok/sNVIDIA H100 SXM~968 tok/sAMD Instinct MI250X~801 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM3 3B?
At Q4_K_M, the download is about 1.80 GB. The full-precision Q8_0 version is 3.00 GB. The smallest option (IQ2_XXS) is 0.83 GB.