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.
516.4K downloads
970 likesSpecifications
- Publisher
- Hugging Face
- Family
- SmolLM
- Parameters
- 3.1B
- Architecture
- SmolLM3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-07-08
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.8 GB | 6.4 GB | 1.31 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.8 GB | 6.5 GB | 1.35 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.9 GB | 6.6 GB | 1.50 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.0 GB | 6.7 GB | 1.54 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.3 GB | 7.0 GB | 1.85 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.6 GB | 7.3 GB | 2.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.0 GB | 7.7 GB | 2.54 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | 8.2 GB | 3.08 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 7.0 GB. 50 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 GB59 devices with unified memory can run SmolLM3 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolLM3 3B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does SmolLM3 3B need?
SmolLM3 3B requires 2.3 GB of VRAM at Q4_K_M, or 6.6 GB at BF16. Full 66K context adds up to 4.7 GB (7.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.2 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M2.3 GBQ4_K_M + full context7.0 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 GBQ3_K_S1.8 GBQ4_K_S2.2 GBQ4_K_M ★2.3 GBQ5_K_S2.6 GBBF166.6 GB★ 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 ~1913 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~285 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.3 × 0.65 = ~2261 tok/s
Estimated speed at Q4_K_M (2.3 GB)
~2261 tok/s~285 tok/s~2261 tok/s~1913 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 1.85 GB. The full-precision BF16 version is 6.15 GB. The smallest option (IQ2_XXS) is 0.85 GB.
- Which GPUs can run SmolLM3 3B?
50 consumer GPUs can run SmolLM3 3B at Q4_K_M (2.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run SmolLM3 3B?
59 devices with unified memory can run SmolLM3 3B at Q4_K_M (2.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.