HuggingFace/Dense

HuggingFaceSmolVLM 2.2B

SmolVLM is a compact open multimodal model that accepts arbitrary sequences of image and text inputs to produce text outputs.

chatvision
2.2B
Parameters
16K
Context length
7
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2024-11-26
Layers
24
KV Heads
32
Head Dim
64
Family
smollm

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.891.8 GBgood
Q5_K_S5.572.0 GBgood
Q5_K_M5.72.1 GBgood
Q6_K6.562.3 GBexcellent
Q8_08.52.8 GBlossless
FP16164.9 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 SMOLVLM 2.2B NOW

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

Community Ratings

Loading ratings...

Benchmarks (7)

IFEval64.3
MMMU38.8
MMLU-PRO10.8
BBH10.5
MATH5.6
MUSR2.5
GPQA1.9

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run smollm:2b-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 SmolVLM 2.2B

Build Hardware for SmolVLM 2.2B
▸ SPEC SHEET

SmolVLM 2.2B2.2B Dense.

▸ SPECIFICATIONS
PARAMETERS
2.2B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
16K tokens
CAPABILITIES
chat, vision
RELEASE DATE
2024-11-26
PROVIDER
HuggingFace
FAMILY
smollm
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.891.8 GB94%
Q5_K_S5.572.0 GB96%
Q5_K_M5.72.1 GB96%
Q6_K6.562.3 GB97%
Q8_08.52.8 GB100%
FP16164.9 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO10.8
MATH5.6
IFEval64.3
BBH10.5
MMMU38.8
GPQA1.9
MUSR2.5