How much VRAM does Gemma 2 2B Instruct need?

About 2.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 3.7 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 2B Instruct run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 2.1 GB, which leaves 3.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.

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

Q8_0 has the lowest published quality loss (0.3%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, AWQ INT4.

Gemma 2 2B Instruct

Google Gemma 2

⚠ Superseded · Prefer Gemma 4 E2B IT

This index would suggest Gemma 4 E2B 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 →

Ultra-compact Gemma 2. Runs on 4GB VRAM; great for edge prototyping.

⬇ 64.3K HF downloads♥ 103 likesbartowski/gemma-2-2b-it-GGUF· stats from 10/20/2026

Consumer GPUMac / Apple SiliconCPU / VPS

Max Context

Quant Variants

GGUF Q8_0

Best Quality

99.7%

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	2.0 GB	3.8%	380 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	3.2 GB	0.3%	320 tok/s	Estimated	Calc HF
AWQ	INT4	4	1.8 GB	5.0%	450 tok/s	Estimated	Calc HF

Similar models

Compare with Gemma 2

Gemma 2 9B Instruct

Google Gemma 2

Consumer GPUMac / Apple Silicon

5.8 GBmin VRAM99.8%accuracy

Google's compact Gemma 2 with sliding window attention. Punches above 9B.

27B

Gemma 2 27B Instruct

Google Gemma 2

Consumer GPUPro GPU

16.2 GBmin VRAM98.7%accuracy

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

Llama 3.2 3B Instruct

Meta Llama 3.2

Consumer GPUMac / Apple Silicon

2.0 GBmin VRAM99.8%accuracy

Tiny but capable. Runs on 4GB VRAM or 8GB RAM, even on phones via llama.cpp.

Qwen2.5 3B Instruct

Alibaba Qwen2.5

Consumer GPUMac / Apple Silicon

2.1 GBmin VRAM99.7%accuracy

Tiny Qwen2.5 for edge devices. Runs on 4GB VRAM or Raspberry Pi class hardware.

Running Gemma 2 2B Instruct locally

At Q4_K_M and 4K of context, Gemma 2 2B Instruct needs about 2.1 GB — 1.5 GB of weights, 0.41 GB of KV cache and a 0.2 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 11 of them, from the Mac M1 8G up — and 78 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 1.44 GB, taking the total to 3.7 GB. That is gentler than the parameter count suggests: only 13 of 26 layers keep a KV cache that grows with context, and 13 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 2 GB.

Which build to download

This index tracks 2 formats for it — GGUF, AWQ — across 3 levels. Q8_0 carries the lowest published perplexity loss at 0.3%. The fastest level listed is AWQ INT4 at about 450 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 2B Instruct need?: About 2.1 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 3.7 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 2B Instruct run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 2.1 GB, which leaves 3.9 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (11 in all). That is the smallest size in this index that clears it comfortably; 78 of 78 do.
Which quantization of Gemma 2 2B Instruct should I use?: Q8_0 has the lowest published quality loss (0.3%), and Q4_K_M is the level most people run. All 3 levels in the index are Q4_K_M, Q8_0, 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: Mac M1 8G8GB · +3.9 Mac M3 8G8GB · +3.9 RTX 40608GB · +5.9 RTX 4060 Ti 8G8GB · +5.9 Radeon RX 9060 XT 8G8GB · +5.9
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