Google27B~17 GB VRAM at Q4_K_M

Gemma 3 27B Instruct — VRAM, speed & local setup

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

Written by Jakub Rusinowski · Last updated March 12, 2025

Google's most capable local model. Near-frontier performance at 27B scale. Requires 16GB VRAM, making it ideal for RTX 4080/4090 and Apple Silicon with 32GB+ unified memory.

Gemma 3 27B Instruct needs about 17 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	11 GB	~65 tok/s	Fits
Q3_K_M 3.41 bpw	13.6 GB	~53 tok/s	Fits
Q4_K_M 4.83 bpw	18.4 GB	~40 tok/s	Fits
Q5_K_M 5.67 bpw	21.3 GB	~35 tok/s	Fits
Q6_K 6.56 bpw	24.3 GB	~5 tok/s	Offload
Q8_0 8.50 bpw	30.8 GB	~4 tok/s	Offload
F16 16.00 bpw	56.1 GB	—	Too big

Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Gemma 3 27B Instruct VRAM calculator →

Get Gemma 3 27B Instruct running

The cheapest catalogued GPU that runs Gemma 3 27B Instruct is the AMD Radeon RX 7900 XT (20 GB).

Buy this hardware AMD Radeon RX 7900 XT 20GB — 20 GB VRAM · 315 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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AMD Radeon RX 7900 XT 20GB

20 GB VRAM · 315 W board power

2026 prices are volatile — check the current listing.

Check price on Amazon

How to run Gemma 3 27B Instruct

Install Ollama, then run:

$ollama run gemma3:27b

Weights on Hugging Face: google/gemma-3-27b-it ↗

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: 27 Billion
Context window: 128,000
Architecture: Dense
Provider: Google
Licence: Gemma Terms
Practical deployment: Single consumer GPU
Specified at: Q4_K_M
System RAM: 32 GB
Record updated: 2025-03-12

LicenceGemma TermsCommercial use permitted

Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

Quality and use cases

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

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Can I run Gemma 3 27B Instruct on my GPU?

Other Gemma 3 sizes

Gemma 3 27B Instruct — frequently asked questions

How much VRAM does Gemma 3 27B Instruct need?

About 17 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 Gemma 3 27B Instruct run on an RTX 4090 (24 GB)?

Yes. Gemma 3 27B Instruct needs about 17 GB at Q4_K_M, inside a 24 GB card, at an estimated 40 tokens/sec.

How do I run Gemma 3 27B Instruct locally?

Install Ollama and run `ollama run gemma3:27b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

What other sizes does Gemma 3 come in?

Gemma 3 1B Instruct (1 GB), Gemma 3 4B Instruct (3 GB), Gemma 3 12B Instruct (8 GB), Gemma 3 27B Instruct (17 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.