HuggingFace/Dense

HuggingFaceSmolLM2 360M

Compact SmolLM for mobile and embedded AI applications.

chatTool Use
0.36B
Parameters
8K
Context length
6
Benchmarks
6
Quantizations
150K
HF downloads
Architecture
Dense
Released
2024-11-21
Layers
32
KV Heads
5
Head Dim
64
Family
smollm

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.890.7 GBgood
Q5_K_S5.570.7 GBgood
Q5_K_M5.70.7 GBgood
Q6_K6.560.8 GBexcellent
Q8_08.50.9 GBlossless
FP16161.2 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN SMOLLM2 360M NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (6)

IFEval38.4
BBH4.2
MUSR2.8
MATH1.5
MMLU-PRO1.3
GPQA0.7

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run smollm:0.36b-q4_K_M

Tag may need adjustment — check ollama.com/library/smollm for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Find the best GPU for SmolLM2 360M

Build Hardware for SmolLM2 360M

Compact SmolLM for mobile and embedded AI applications.

▸ SPEC SHEET

SmolLM2 360M0.36B Dense.

▸ SPECIFICATIONS
PARAMETERS
0.36B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
8K tokens
CAPABILITIES
chat
RELEASE DATE
2024-11-21
PROVIDER
HuggingFace
FAMILY
smollm
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.890.7 GB94%
Q5_K_S5.570.7 GB96%
Q5_K_M5.70.7 GB96%
Q6_K6.560.8 GB97%
Q8_08.50.9 GB100%
FP16161.2 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO1.3
MATH1.5
IFEval38.4
BBH4.2
GPQA0.7
MUSR2.8