Gemma 4 E2B

Gemma 4 E2B loads in 2.3 GB at Q4_K_M and is comfortable from 8 GB of memory, Macs included from the Mac Mini M6 16GB. Below: the full memory math, the cheapest card that runs it well, and the fastest machine we track.

PARAMETERS

2.3B

FORMAT

Q4_K_M

MIN MEMORY

4 GB

BEST FOR

IoT, Mobile, Edge

Memory math

Weights (Q4_K_M)

2.3 GB

+ KV cache (16k)

~1.8 GB

Total at 16k

~4.0 GB

Comfortable from

8 GB

KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).

Hardware snapshot

Cheapest GPU

AMD Radeon RX 7900 XT

~214 tok/s est. — ~$550 used

Fastest

NVIDIA GeForce RTX 4090

~300 tok/s est.

Runs on a Mac?

Mac Mini M6 16GB

~97 tok/s est.

Go deeper

Every Gemma 4 E2B quant by real GGUF file sizeAlso tracked: Gemma 4 E2B (Q8) (Q8_0, 4.6 GB, min 8 GB)

More Gemma models

Gemma 3 1B Instruct Gemma 2 2B Instruct Gemma 3 4B Instruct Gemma 4 E4B Gemma 2 9B Instruct Gemma 3 12B Instruct Gemma 4 12B Gemma 4 26B-A4B Gemma 2 27B Instruct Gemma 3 27B Instruct Gemma 4 31B

Frequently asked questions

How much memory does Gemma 4 E2B need?

2.3 GB for the Q4_K_M weights, plus ~1.8 GB of KV-cache at 16k context — about 4.0 GB total. Comfortable from 8 GB of VRAM or unified memory.

Does Gemma 4 E2B run on a Mac?

Yes — from the Mac Mini M6 16GB (~97 tok/s est.). Unified memory means the RAM budget is the only limit.

What is the cheapest GPU for Gemma 4 E2B?

The AMD Radeon RX 7900 XT is the cheapest tracked card that runs Gemma 4 E2B comfortably — ~214 tok/s est. at ~$550 used.

Cite this page

ModelFit: Gemma 4 E2B — specs, memory math and hardware verdicts.
https://modelfit.io/models/gemma4-e2b/ (dataset updated 2026-09-03, CC BY 4.0).