SmolLM2 360M Instruct — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated November 20, 2024

Model library → SmolLM2 → SmolLM2 360M Instruct

Incredibly lightweight. Runs on any device including old phones and embedded systems. Useful for on-device classification, simple Q&A, and keyword extraction where even 1B is too large.

SmolLM2 360M Instruct needs about 1 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters360 Million
Context window8,192
ArchitectureDense
ProviderHuggingFace
LicenceApache 2.0
Specified atQ4_K_M
System RAM2 GB
Record updated2024-11-20

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K0.1 GB1.3 GB~381 tok/s (est.)Fits comfortably
Q3_K_M0.2 GB1.3 GB~374 tok/s (est.)Fits comfortably
Q4_K_M0.2 GB1.4 GB~363 tok/s (est.)Fits comfortably
Q5_K_M0.3 GB1.4 GB~356 tok/s (est.)Fits comfortably
Q6_K0.3 GB1.4 GB~350 tok/s (est.)Fits comfortably
Q8_00.4 GB1.5 GB~336 tok/s (est.)Fits comfortably
F160.7 GB1.9 GB~292 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the SmolLM2 360M Instruct VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs SmolLM2 360M Instruct is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

How to Run SmolLM2 360M Instruct

Install Ollama, then run:

ollama run smollm2:360m

Weights on Hugging Face: HuggingFaceTB/SmolLM2-360M-Instruct.

Best for: embedded, classification, edge devices.

Can I Run SmolLM2 360M Instruct on My GPU?

Other SmolLM2 Sizes

SmolLM2 360M Instruct — Frequently Asked Questions

How much VRAM does SmolLM2 360M Instruct need?
About 1 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does SmolLM2 360M Instruct run on an RTX 4090 (24 GB)?
Yes. SmolLM2 360M Instruct needs about 1 GB at Q4_K_M, inside a 24 GB card, at an estimated 363 tokens/sec.
How do I run SmolLM2 360M Instruct locally?
Install Ollama and run `ollama run smollm2:360m`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does SmolLM2 come in?
SmolLM2 1.7B Instruct (2 GB), SmolLM2 360M Instruct (1 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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