How much VRAM does Gemma 2 27B Instruct need?

About 18.8 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 24.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Gemma 2 27B Instruct run on a 24GB GPU?

Yes — at Q4_K_M and 4K context it needs about 18.8 GB, which leaves 5.2 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.

Which quantization of Gemma 2 27B Instruct should I use?

Q5_K_M has the lowest published quality loss (1.3%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q5_K_M, AWQ INT4.

Gemma 2 27B Instruct

27B

Google Gemma 2

⚠ Superseded · Prefer Gemma 3 27B IT

This index would suggest Gemma 3 27B IT instead today — longer context (8K → 128K tokens). The numbers below are still accurate; they are just for a model you probably should not start with. Compare the two →

Largest open Gemma 2. Strong reasoning; needs 24GB+ VRAM at Q4.

⬇ 157.0K HF downloads♥ 175 likesbartowski/gemma-2-27b-it-GGUF· stats from 10/20/2026

Consumer GPUPro GPU

Max Context

Quant Variants

GGUF Q5_K_M

Best Quality

98.7%

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	18.5 GB	2.9%	48 tok/s	Estimated	Calc HF
GGUF	Q5_K_M	5.68	21.2 GB	1.3%	42 tok/s	Estimated	Calc HF
AWQ	INT4	4	16.2 GB	4.0%	58 tok/s	Estimated	Calc HF

Similar models

Compare with Gemma 2

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Gemma 2 2B Instruct

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32B

Qwen2.5 32B Instruct

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Near-GPT-4 reasoning on a 24GB VRAM card (Q4_K_S). Groundbreaking value.

24B

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Running Gemma 2 27B Instruct locally

At Q4_K_M and 4K of context, Gemma 2 27B Instruct needs about 18.8 GB — 15.7 GB of weights, 1.44 GB of KV cache and a 1.7 GB activation buffer. The smallest cards in this index that clear that comfortably are 24 GB ones — 4 of them, from the RTX 3090 up — and 31 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 5.12 GB, taking the total to 24.4 GB. That is gentler than the parameter count suggests: only 23 of 46 layers keep a KV cache that grows with context, and 23 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 8K; holding all of it at Q4_K_M would need about 20 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q5_K_M carries the lowest published perplexity loss at 1.3%. The fastest level listed is AWQ INT4 at about 58 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.

Common questions

How much VRAM does Gemma 2 27B Instruct need?: About 18.8 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 24.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Gemma 2 27B Instruct run on a 24GB GPU?: Yes — at Q4_K_M and 4K context it needs about 18.8 GB, which leaves 5.2 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.
Which quantization of Gemma 2 27B Instruct should I use?: Q5_K_M has the lowest published quality loss (1.3%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q5_K_M, AWQ INT4.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 309024GB · +5.2 Radeon RX 7900 XTX24GB · +5.2 RTX 409024GB · +5.2 Tesla P40 24G24GB · +5.2 Mac M5 32G32GB · +5.2
Tight but possible: Radeon RX 7900 XT20GB Mac M4 24G24GB Mac M4 Pro 24G24GB
Just misses: RTX 4060 Ti 16G+2.8GB over Radeon RX 7600 XT+2.8GB over
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml