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Text model · Gemma

Gemma 4 E4B: RAM and VRAM requirements

Gemma 4 E4B needs about 6.5 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~4.98 GB to download; KV cache and overhead add the rest), or about 9.7 GB at Q8_0. The lightest hardware that runs it is Nvidia GeForce RTX 3060 (12GB).

Gemma family · 8B params · released Mar 2026.

Shopping for hardware? See what runs Gemma 4 E4B →

License Apache-2.0 · Commercial OK ↓ 5.8M/mo ♥ 1.4K on HuggingFace
Q4_K_M GGUF
4.98 GB
Q8_0 GGUF
8.19 GB
Memory @ Q4 (4k)
~6.5 GB
Context
128 k

Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.

Will it run on your device?

Gemma 4 E4B runs on 33 of 40 tracked devices at Q4_K_M.

33 run well 0 tight fit 7 too small
Fit check Q4_K_M
Yes, it runs fast
needs 6.5 GB usable 10.5 GB
See the full breakdown
$ollama run gemma4:e4b

Memory breakdown

Weights (Q4_K_M)4.98 GB
+
KV cache (4k)0.7 GB
+
Overhead0.8 GB
=
Total6.5 GB

How context length changes it

4k context ~6.5 GB 32k context ~11.2 GB 128k context ~27.5 GB

Longer context grows the KV cache quickly: Gemma 4 E4B needs ~6.5 GB at 4k but ~27.5 GB at 128k, which can push it past a device that fits it at a short context.

Speed drops too: every token re-reads the KV cache, so at 128k context Gemma 4 E4B generates at roughly ~19% of its short-context speed. How this is estimated.

Quantization sizes

GGUF quantson disk
Quantization Size on disk
Q2_K 3.4 GB est
Q3_K_M 3.9 GB est
Q4_K_M (default) 4.98 GB
Q5_K_M 5.7 GB est
Q6_K 6.6 GB est
Q8_0 8.19 GB
FP16 15.05 GB

Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.

Run it

Ollama
$ ollama run gemma4:e4b
llama.cpp
$ llama-cli -hf unsloth/gemma-4-E4B-it-GGUF:Q4_K_M
LM Studio
$ lms get unsloth/gemma-4-E4B-it-GGUF

Which devices can run Gemma 4 E4B?

Apple Silicon Macs

RAM-only laptops

iPhone & iPad

Android

NVIDIA GPUs

AMD GPUs

Same job, different size

Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.

Next size down Qwen2.5 Coder 7B 7B Next size up DeepSeek-R1-0528-Qwen3-8B 8.19B

Similar models

Head-to-head

FAQ

How much VRAM or RAM does Gemma 4 E4B need?

At Q4_K_M, Gemma 4 E4B needs about 6.5 GB (weights ~4.98 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~9.7 GB.

What is the Q4_K_M GGUF file size of Gemma 4 E4B?

The Q4_K_M GGUF file is about 4.98 GB to download, and the Q8_0 GGUF is about 8.19 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~6.5 GB of memory at Q4_K_M.

Can Gemma 4 E4B run on a laptop?

Yes, Gemma 4 E4B fits on a 16 GB machine at Q4_K_M and runs on Apple Silicon or a 12 GB+ GPU comfortably.

Can I use Gemma 4 E4B commercially?

Yes. Gemma 4 E4B is licensed Apache-2.0, which permits commercial use.

Understand the numbers

Short guides to the ideas behind Gemma 4 E4B's memory and quant figures.

Gemma 4's larger on-device model (PLE): 4.5B effective params, 8B raw with per-layer embeddings. Multimodal (vision, ~1GB mmproj extra). Sizes from unsloth HF repo; the Ollama gemma4:e4b blob is 9.6GB with full-precision embeddings bundled, same pattern as Gemma 3n. 128K context. Ollama v0.31.1 (2026-06-30) roughly doubled Gemma 4 speed on Apple Silicon via MLX multi-token prediction.

Sources

Last validated 2026-08-03. Memory figures are estimates. See methodology.