How much VRAM does Gemma 3 12B IT need?

About 8.5 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.4 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 3 12B IT run on a 10GB GPU?

Yes — at Q4_K_M and 4K context it needs about 8.5 GB, which leaves 1.5 GB spare on a Arc B570 10G, or on any of the other 10 GB cards here (2 in all). That is the smallest size in this index that clears it comfortably; 67 of 78 do.

Which quantization of Gemma 3 12B IT 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 3 12B IT

12B

Google Gemma 3

Mid-size Gemma 3 with vision. Fits 16GB at Q4; excellent multilingual chat.

Consumer GPUMac / Apple Silicon

131K

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	8.8 GB	2.9%	105 tok/s	Estimated	Calc HF
GGUF	Q5_K_M	5.68	10.2 GB	1.3%	92 tok/s	Estimated	Calc HF
AWQ	INT4	4	8.0 GB	3.9%	128 tok/s	Estimated	Calc HF

Similar models

Compare with Gemma 3

Gemma 3 4B IT

Google Gemma 3

Consumer GPUMac / Apple Silicon

3.0 GBmin VRAM99.8%accuracy

Google Gemma 3 multimodal 4B. 128K context; strong vision + text on 8GB cards.

27B

Gemma 3 27B IT

Google Gemma 3

Consumer GPUPro GPU

13.0 GBmin VRAM97.2%accuracy

Gemma 3 large instruct with long context and multimodal support. Q4 ~16GB — dual-GPU or 24GB card with short ctx.

Qwen3-VL 8B Instruct

Alibaba Qwen3-VL

Consumer GPUMac / Apple Silicon

5.3 GBmin VRAM99.7%accuracy

Current-generation vision-language model that still fits a single 8–12GB card at Q4 (~5.9GB). The realistic multimodal option for people without a 24GB GPU — note the vision encoder adds VRAM the KV-cache math below does not model.

Qwen3.5 9B

Alibaba Qwen3.5

Consumer GPUMac / Apple Silicon

6.6 GBmin VRAM—accuracy

The newest small Qwen and a common pick for 8 to 16 GB cards. Only 8 of its 32 layers keep a growing KV cache, so it can use a long context on modest hardware. At Q4_K_M it needs about 6.6 GB at 4K and 7.5 GB at 32K, comfortable on an 8 GB card. Its full 256K window needs about 15.2 GB. Q8_0 needs about 10.4 GB, comfortable from 12 GB up. Reasoning is off by default on the small Qwen3.5 sizes, and it reads images through a separate vision encoder. Sizes come from the published GGUF file sizes, and no per-level quality loss has been published.

Running Gemma 3 12B IT locally

At Q4_K_M and 4K of context, Gemma 3 12B IT needs about 8.5 GB — 7.0 GB of weights, 0.72 GB of KV cache and a 0.8 GB activation buffer. The smallest cards in this index that clear that comfortably are 10 GB ones — 2 of them, from the Arc B570 10G up — and 67 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 1.75 GB, taking the total to 10.4 GB. That is gentler than the parameter count suggests: only 8 of 48 layers keep a KV cache that grows with context, and 40 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 128K; holding all of it at Q4_K_M would need about 17 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 128 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 3 12B IT need?: About 8.5 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 10.4 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 3 12B IT run on a 10GB GPU?: Yes — at Q4_K_M and 4K context it needs about 8.5 GB, which leaves 1.5 GB spare on a Arc B570 10G, or on any of the other 10 GB cards here (2 in all). That is the smallest size in this index that clears it comfortably; 67 of 78 do.
Which quantization of Gemma 3 12B IT 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: Arc B570 10G10GB · +1.5 RTX 3080 10G10GB · +1.5 RTX 3060 12G12GB · +3.5 Radeon RX 6700 XT12GB · +3.5 Radeon RX 7700 XT12GB · +3.5
Just misses: RTX 4060+0.5GB over RTX 4060 Ti 8G+0.5GB over
Ships in: GGUF AWQ
Compared here: GGUF vs AWQ
A top pick for: Best local LLM for 12GB
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