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 5.2 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 5.9 GB spare on a RTX 5060 Ti 8G. That is the smallest card in this index that clears it comfortably; 63 of 63 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

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

⬇ 72.9K HF downloads♥ 102 likesbartowski/gemma-2-2b-it-GGUF· stats from 9/21/2026

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 = site benchmarks · Estimated = formula · 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

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Qwen2.5 3B Instruct

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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 card in this index that clears that comfortably is the RTX 5060 Ti 8G at 8 GB, and 63 of the 63 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 2.84 GB, taking the total to 5.2 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 8K; holding all of it at Q4_K_M would need about 3 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 measured here is AWQ INT4 at 450 tok/s on an RTX 4090, batch 1. 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 and CUDA only.

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 5.2 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 5.9 GB spare on a RTX 5060 Ti 8G. That is the smallest card in this index that clears it comfortably; 63 of 63 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: RTX 5060 Ti 8G8GB · +5.9 RTX 50608GB · +5.9 RTX 4060 Ti 8G8GB · +5.9 RTX 40608GB · +5.9 RTX 3070 Ti8GB · +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 2026-09-21 RSS → /feed.xml