HuggingFace
SmolLM3 3B
423.9KDownloads961LikesJul 2025Veröffentlicht128K TokenKontextApache 2.0Lizenz21 EinstiegQualität
SmolLM3 3B (3B parameters) requires approximately 5.6 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 7 GB of VRAM.
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— kopieren & einfügen, um lokal auszuführenCopy-paste commands to run SmolLM3 3B on your machine.
Run
lms load SmolLM3-3B && lms server startQuick specs
Parameters3B
Architecturedense
Context128K tokens
Modalitytext
Min RAM1.2 GB
Rec. RAM1.8 GB (Q4_K_M)
LicenseApache 2.0
FamilySmolLM
✓ Chat✓ Reasoning
About this model
- •Dual-mode reasoning: extended thinking can be toggled on/off
- •128K context via YARN extrapolation from 64K training
- •6 natively supported languages: English, French, Spanish, German, Italian, Portuguese
- •Fully open: weights, training details, and public data mixture
Schnellauswahl
Beste Hardware
Top-Empfehlungen für SmolLM3 3B
Dieses Modell ausführen
Quantisierungsoptionen
VRAM-Schätzungen nach Quantisierungsstufe
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | — |
Q3_K_S | 3 | 1.5 GB | Low | — |
NVFP4 | 4 | 1.7 GB | Medium | — |
Q4_K_M | 4 | 1.8 GB | Medium | — |
Q5_K_M | 5 | 2.2 GB | High | — |
Q6_K | 6 | 2.5 GB | High | — |
Q8_0 | 8 | 3.2 GB | Very High | — |
F16 | 16 | 6.1 GB | Maximum | — |
Quality benchmarks
SmolLM3 3B benchmark scores
Coding
SWE-bench Verified—
HumanEval+30.5%
Aider Polyglot—
LiveCodeBench—
Reasoning
MMLU-Pro32.7%
GPQA Diamond35.7%
MATH-500—
ARC Challenge—
General
Chatbot Arena—
IFEval76.7%
Source: official · 2025-07-02
Hardware-Kompatibilität
Eignungsschätzungen für alle Hardware
Computing compatibility...
Speicheraufschlüsselung
Reference: RTX 2060 6GB
Weights1.8 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom0.6 GB
Häufig gestellte Fragen
FAQ — SmolLM3 3B
Siehe auch