Allen AI13B~9 GB VRAM at Q4_K_M

OLMo 2 13B Instruct — VRAM, speed & local setup

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated November 26, 2024

The larger OLMo 2 variant. Beats Llama 3.1 8B on most academic benchmarks while maintaining full data and code transparency. Ideal for NLP research institutions.

OLMo 2 13B Instruct needs about 9 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.

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VRAM and speed by quantization

Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.

Quant	VRAM	Speed (est.)	Fit
Q2_K 2.63 bpw	12 GB	~79 tok/s	Fits
Q3_K_M 3.41 bpw	13.4 GB	~69 tok/s	Fits
Q4_K_M 4.83 bpw	15.8 GB	~57 tok/s	Fits
Q5_K_M 5.67 bpw	17.2 GB	~51 tok/s	Fits
Q6_K 6.56 bpw	18.7 GB	~46 tok/s	Fits
Q8_0 8.50 bpw	22.1 GB	~39 tok/s	Tight
F16 16.00 bpw	34.9 GB	~3 tok/s	Offload

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. OLMo 2 13B Instruct VRAM calculator →

Get OLMo 2 13B Instruct running

The cheapest catalogued GPU that runs OLMo 2 13B Instruct is the Intel Arc B580 (12 GB).

Buy this hardware Intel Arc B580 12GB — 12 GB VRAM · 190 W board power Deploy in the cloud now RTX 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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Intel Arc B580 12GB

12 GB VRAM · 190 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

How to run OLMo 2 13B Instruct

Install Ollama, then run:

$ollama run olmo2:13b

Weights on Hugging Face: allenai/OLMo-2-1124-13B-Instruct ↗

Specifications

Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.

Parameters: 13 Billion
Context window: 4,096
Architecture: Dense
Provider: Allen AI
Licence: Apache 2.0
Practical deployment: Laptop
Specified at: Q4_K_M
System RAM: 24 GB
Record updated: 2024-11-26

LicenceApache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Quality and use cases

Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.

Best forresearcheducationrag

Can I run OLMo 2 13B Instruct on my GPU?

Other OLMo 2 sizes

OLMo 2 7B Instruct~5 GB at Q4_K_M

OLMo 2 13B Instruct — frequently asked questions

How much VRAM does OLMo 2 13B Instruct need?

About 9 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 OLMo 2 13B Instruct run on an RTX 4090 (24 GB)?

Yes. OLMo 2 13B Instruct needs about 9 GB at Q4_K_M, inside a 24 GB card, at an estimated 57 tokens/sec.

How do I run OLMo 2 13B Instruct locally?

Install Ollama and run `ollama run olmo2:13b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does OLMo 2 come in?

OLMo 2 7B Instruct (5 GB), OLMo 2 13B Instruct (9 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.