SmolLM3 3B Base — Hardware Requirements & GPU Compatibility
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SmolLM3 3B Base is the pretrained foundation model from Hugging Face's third-generation SmolLM family. Without instruction tuning or chat alignment, it serves as a versatile starting point for researchers and developers who want to fine-tune the model for specific domains, tasks, or behavioral profiles. With 3 billion parameters and the architectural improvements introduced in SmolLM3, this base model offers strong general language capabilities in a package that remains practical to train and adapt on consumer-grade hardware. It is an excellent choice for custom fine-tuning projects where off-the-shelf chat behavior is not needed.
89.8K downloads
150 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 3B
- Architecture
- SmolLM3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-08-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM3 3B Base 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 Base?
Q4_K_M · 2.3 GBSmolLM3 3B Base (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 Base?
Q4_K_M · 2.3 GB33 devices with unified memory can run SmolLM3 3B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does SmolLM3 3B Base need?
SmolLM3 3B Base 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 Base?
For SmolLM3 3B Base, 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 Base on a Mac?
SmolLM3 3B Base 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 Base locally?
Yes — SmolLM3 3B Base 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 Base?
At Q4_K_M, SmolLM3 3B Base 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 Base?
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.