Gemma 4 E2B
Gemma 4 E2B loads in 2.3 GB at Q4_K_M, comfortably from 8 GB of memory, Macs included, starting at the Mac Mini M6 16GB. Below is the memory math we ran for it, the smallest card that fits, and the fastest machine we track.
How much memory does Gemma 4 E2B need?
Gemma 4 E2B is a 2.3B dense model built for iot, mobile, edge. The weights alone take 2.3 GB at Q4_K_M; add context and the OS, and it belongs in the 8 GB memory class. Every number below comes from the ModelFit engine: speeds are estimates, labeled est., because we do not benchmark hardware ourselves.
KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).
Which machines run Gemma 4 E2B?
Verdicts come from the same engine that powers our fit checker: memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card
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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 smallest GPU for Gemma 4 E2B?
The NVIDIA GeForce RTX 4060 (8 GB VRAM) is the smallest tracked card that runs Gemma 4 E2B comfortably, ~87 tok/s est.
Which Gemma 4 E2B quant should I pick?
The tracked Q4_K_M build at 2.3 GB is the one we recommend for most machines. Every published build, ranked by real GGUF file size rather than a formula, is on the quant comparison page linked below.
Cite this page
ModelFit: Gemma 4 E2B, specs, memory math and hardware verdicts. https://modelfit.io/models/gemma4-e2b/ (dataset updated 2026-10-13, CC BY 4.0).