Gemma 4 E4B IT
Gemma 4 E4B IT by Google DeepMind (8B parameters). At Q4_K_M with an 8K context it needs about 5.5 GiB of memory. A small model built for laptops and other devices: 4.5B effective parameters (8B with per-layer embeddings), with text, image and audio input.
Last updated: 2026-10-13
Model facts
Run it
ollama run gemma4:e4b
Memory needed
Weights + KV cache + runtime overhead, in GiB. Lower quantization = less memory, slightly lower quality.
| Quantization | 4K context | 8K context | 32K context |
|---|---|---|---|
| Q4_K_M | 5.4 GiB | 5.5 GiB | 6.2 GiB |
| Q5_K_M | 6.2 GiB | 6.3 GiB | 7.0 GiB |
| Q6_K | 7.1 GiB | 7.2 GiB | 7.9 GiB |
| Q8_0 | 9.0 GiB | 9.1 GiB | 9.7 GiB |
| FP16 | 16.3 GiB | 16.4 GiB | 17.1 GiB |
Which hardware runs it
Best quantization that fits each device with a 8K context. Estimates, not benchmarks. See the methodology.
Frequently asked questions
How much VRAM does Gemma 4 E4B IT need?
With an 8K context, about 5.5 GiB at Q4_K_M and 9.1 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can Gemma 4 E4B IT run on an GeForce RTX 4090?
Yes. Gemma 4 E4B IT fits on the GeForce RTX 4090 (24 GB) at FP16 with an 8K context, using about 16.4 GiB, at an estimated 44 tokens/s.
Can I run Gemma 4 E4B IT on a Mac?
Yes. Gemma 4 E4B IT fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 16.4 GiB, at an estimated 33 tokens/s.
Can Gemma 4 E4B IT be used commercially?
Gemma 4 E4B IT is released under Apache-2.0. The licence allows commercial use (read its conditions).
Sources
- https://ai.google.dev/gemma/docs/releases
- https://ai.google.dev/gemma/docs/gemma_4_license
- https://huggingface.co/google/gemma-4-E4B-it
- https://huggingface.co/google/gemma-4-E4B-it/resolve/main/config.json
- https://huggingface.co/api/models/google/gemma-4-E4B-it?expand[]=safetensors
- https://ollama.com/library/gemma4/tags