SmolLM3 3B
By HuggingFace · France
Updated 2026-07-13
Overview
HuggingFace's 3B model with dual think/no-think modes, 128k context, and full open data and recipe — punching at MMLU 59.7 and GSM8K 70.9.
When to pick this model
- Edge devices and laptops needing real reasoning at 3B
- Long-context tasks where larger models aren't viable
- Multilingual chat across the six supported European languages
- Educational and research use needing fully open training
- Latency-sensitive applications wanting toggleable thinking
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 2 GB |
| Q5_K_M | 2.5 GB |
| Q8_0 | 4 GB |
| FP16 (no quantization) | 6 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, SmolLM3 3B fits an 8 GB consumer card at Q4_K_M (2 GB). Stepping up to Q8_0 nearly doubles the footprint to 4 GB, and unquantized FP16 weights take 6 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, SmolLM3 3B needs roughly 5 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 25 tokens/sec on entry-level GPUs, on the order of 70 tokens/sec on a mid-range card, and up to 160 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches SmolLM3 3B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for SmolLM3 3B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (6 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (6 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (6 GB used) |
| 32 GB | RTX 5090 | FP16 (6 GB used) |
Which GPU should you buy to run SmolLM3 3B?
To run SmolLM3 3B locally at Q4, you need ~2 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 59.7 |
| GSM8K | 70.9 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put SmolLM3 3B in context: its MMLU score of 59.7 ranks #32 of the 34 catalog models with a published MMLU result (catalog median 73.4); its GSM8K score of 70.9 ranks #9 of the 9 catalog models with a published GSM8K result (catalog median 83.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Compact think mode delivers reasoning at 3B scale
- Native support for six languages
- Apache 2.0 with fully open training data and recipe
- 128k context unusual at this size
- Strong MMLU and GSM8K for the parameter count
Limitations
- No official Ollama distribution — needs manual setup
- Quality ceiling typical of 3B dense models on hard tasks
- Smaller community than competing 3B releases
Typical workloads
In our catalog grid, SmolLM3 3B is filed under Edge Reasoning, Compact Think-Mode — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense 3B · dual-mode think/no-think · 64k native + YaRN
Training: Fully open (data + recipe).
The reasoning-capable 3B to beat — ideal for edge deployments that still need think-mode and 128k context.
Quick start
# HuggingFace : HuggingFaceTB/SmolLM3-3BOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does SmolLM3 3B need?
At the recommended Q4_K_M quantization, SmolLM3 3B needs about 2 GB of VRAM. Q8_0 takes 4 GB, and unquantized FP16 weights take 6 GB.
Can SmolLM3 3B run without a GPU?
Yes — with roughly 5 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does SmolLM3 3B support?
SmolLM3 3B supports a 125k-token context window (128,000 tokens).
Can I use SmolLM3 3B commercially?
Yes. SmolLM3 3B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is SmolLM3 3B on consumer hardware?
Our compatibility engine estimates on the order of 70 tokens/sec on a mid-range GPU and up to 160 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of SmolLM3 3B should I download first?
Start with Q4_K_M (2 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at FP16.