About Gemma 4 E4B
Gemma 4 E4B is the smallest registry member of Google DeepMind's Gemma 4 family of open models, released under the Apache 2.0 licence and positioned by Google for environments ranging from high-end phones to laptops. It is genuinely multimodal for its size — text, image and native audio input — and shares the family's hybrid attention design, which interleaves local sliding-window layers with occasional full-attention layers.
The intended uses Google lists — text generation, coding, reasoning, conversational AI and audio interaction — read like a flagship's, which is the point: this is the family's capability floor, not a toy tier.
Gemma 4 E4B is Google's 8B-parameter model released in April 2026, with a 128K-token context window. It uses a hybrid-attention design — only a fraction of its layers cache the full context, so long conversations cost far less VRAM than a classic dense model: its KV cache is about 0.2 GB at an 8K context, 1.9 GB at 128K, and 1.9 GB at the full 128K window (FP16 cache).
This is the Gemma most people can run without thinking about it: at the popular quantisation levels the totals above fit older mid-range cards, thin-and-light laptops with unified memory, and everything upward. The hybrid attention layout does real work here — with only a small fraction of layers holding full-context cache, memory stays nearly flat as conversations grow, so the full context window is genuinely usable on modest hardware rather than a spec-sheet number. It runs in all the mainstream local stacks, and its audio-capable multimodal variant makes it a common pick for on-device assistants.
For most people Q4_K_M is the sweet spot — the most popular quality/size trade-off — while Q8 is near-lossless if you have the memory. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see the methodology.
Frequently asked questions
How much VRAM does Gemma 4 E4B need?
At Q4_K_M with an 8K context, Gemma 4 E4B needs about 7 GB (weights 5 GB + KV cache + overhead). The smallest common hardware that fits is a RTX 3060 12GB.
Can an RTX 4090 (24GB) run Gemma 4 E4B?
Yes. An RTX 4090's 24 GB runs Gemma 4 E4B at FP16 / BF16 (about 19 GB at 8K context) — at full FP16 precision.
Can a Mac run Gemma 4 E4B?
Yes — Apple Silicon with 16 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~12 GB) runs Gemma 4 E4B at Q4_K_M.
How much VRAM does Gemma 4 E4B need at its full 128K context?
The KV cache grows with context: about 0.2 GB at 8K tokens versus 1.9 GB at the full 128K window (FP16 cache). Add that to the weights (5 GB at Q4_K_M) plus overhead — long contexts can cost more than a whole quantisation step.
Related
VRAM calculator for any model ·Token counter
Similar-size local models: Llama 3.1 8B (8.03B) · DeepSeek R1 Distill Qwen3 8B (8.2B) · Gemma 4 12B (12B) · Phi-4 (14.7B) · Qwen3.5 4B (4B)
Last updated 2026-09-03. Architecture figures from the model's published config.json; see the methodology.