FitMyLLM
HuggingFace/Dense

HuggingFaceSmolVLM 500M

SmolVLM-500M is a tiny multimodal model, member of the SmolVLM family. It accepts arbitrary sequences of image and text inputs to produce text outputs. It's designed for efficiency.

chatvision
0.5B
Parameters
8K
Context length
0
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2024-11-26
Layers
32
KV Heads
5
Head Dim
64
Family
smollm

Quantization Options

Context length:
QuantBitsVRAM @ 8KQuality
Q4_K_M4.89
1.0 GB
0.8 + 0.2 KV
good
Q5_K_S5.57
1.0 GB
0.8 + 0.2 KV
good
Q5_K_M5.7
1.0 GB
0.8 + 0.2 KV
good
Q6_K6.56
1.1 GB
0.9 + 0.2 KV
excellent
Q8_08.5
1.2 GB
1.0 + 0.2 KV
lossless
FP1616
1.6 GB
1.5 + 0.2 KV
lossless

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

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Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run smollm:0.5b-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 500M

Build Hardware for SmolVLM 500M
▸ SPEC SHEET

SmolVLM 500M0.5B Dense.

▸ SPECIFICATIONS
PARAMETERS
0.5B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
8K tokens
CAPABILITIES
chat, vision
RELEASE DATE
2024-11-26
PROVIDER
HuggingFace
FAMILY
smollm
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.890.8 GB94%
Q5_K_S5.570.8 GB96%
Q5_K_M5.70.8 GB96%
Q6_K6.560.9 GB97%
Q8_08.51.0 GB100%
FP16161.5 GB100%
§ 03COMPATIBLE GPUs
30 @ Q4_K_M