How much VRAM does Gemma 3 4B IT need?

About 3.0 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 3.6 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 4B IT run on a 8GB GPU?

Yes — at Q4_K_M and 4K context it needs about 3.0 GB, which leaves 3.0 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 3 4B IT should I use?

Q8_0 has the lowest published quality loss (0.2%), 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 3 4B IT

Google Gemma 3

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

Consumer GPUMac / Apple SiliconCPU / VPS

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

99.8%

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	3.4 GB	3.2%	175 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	5.2 GB	0.2%	145 tok/s	Estimated	Calc HF
AWQ	INT4	4	3.0 GB	4.2%	210 tok/s	Estimated	Calc HF

Similar models

Compare with Gemma 3

12B

Gemma 3 12B IT

Google Gemma 3

Consumer GPUMac / Apple Silicon

8.0 GBmin VRAM98.7%accuracy

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

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.

E4B

Gemma 4 E4B IT

Google Gemma 4

Consumer GPUMac / Apple Silicon

4.6 GBmin VRAM—accuracy

Google's on-device Gemma 4: about 4.7B parameters doing the work plus a 2.8B per-layer embedding table, a 128K window, and image and audio input. Only 4 of its 42 layers keep a cache that grows with context, so the full 128K window costs about 2.0 GB of cache. At Q4 it is about 4.9 GB at 4K and 7.0 GB at the full window — an 8 GB card runs it comfortably at everyday context lengths. Sizes here count the whole model in GPU memory, which is what a Mac or a full GPU load uses; llama.cpp keeps the embedding table in system RAM instead, so on a discrete card it needs roughly 1.6 GB less VRAM than shown at Q4. Sizes use the calculator's generic rates — no GGUF file size could be checked.

E2B

Gemma 4 E2B IT

Google Gemma 4

Consumer GPUMac / Apple Silicon

2.8 GBmin VRAM—accuracy

The smallest Gemma 4: about 2.3B parameters doing the work plus a 2.3B per-layer embedding table, a 128K window, and image and audio input. Its cache barely grows — 3 of 35 layers track the full context, about 0.8 GB at 128K. At Q4 it is about 3.0 GB at 4K and 3.8 GB at the full window — comfortable on any card in this index and on an 8 GB Mac. Sizes count the whole model in GPU memory; llama.cpp keeps the embedding table in system RAM, so on a discrete card it needs roughly 1.3 GB less VRAM than shown at Q4. Sizes use the calculator's generic rates — no GGUF file size could be checked.

Running Gemma 3 4B IT locally

At Q4_K_M and 4K of context, Gemma 3 4B IT needs about 3.0 GB — 2.5 GB of weights, 0.25 GB of KV cache and a 0.3 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 0.54 GB, taking the total to 3.6 GB. That is gentler than the parameter count suggests: only 5 of 34 layers keep a KV cache that grows with context, and 29 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 6 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 0.2%. The fastest level listed is AWQ INT4 at about 210 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 4B IT need?: About 3.0 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 3.6 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 4B IT run on a 8GB GPU?: Yes — at Q4_K_M and 4K context it needs about 3.0 GB, which leaves 3.0 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 3 4B IT should I use?: Q8_0 has the lowest published quality loss (0.2%), 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 · +3.0 Mac M3 8G8GB · +3.0 RTX 40608GB · +5.0 RTX 4060 Ti 8G8GB · +5.0 Radeon RX 9060 XT 8G8GB · +5.0
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