Gemma 4 26B A4B IT
Gemma 4 26B A4B IT by Google DeepMind (25.2B parameters, MoE with 3.8B active per token). At Q4_K_M with an 8K context it needs about 15.7 GiB of memory. A MoE with 3.8B of its 25.2B parameters active: it fits a 16 GB card at 4-bit and generates quickly for its size.
Last updated: 2026-10-13
Model facts
Run it
ollama run gemma4:26b
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 | 15.7 GiB | 15.7 GiB | 15.9 GiB |
| Q5_K_M | 18.3 GiB | 18.3 GiB | 18.5 GiB |
| Q6_K | 21.0 GiB | 21.1 GiB | 21.3 GiB |
| Q8_0 | 26.9 GiB | 27.0 GiB | 27.2 GiB |
| FP16 | 50.0 GiB | 50.1 GiB | 50.3 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 26B A4B IT need?
With an 8K context, about 15.7 GiB at Q4_K_M and 27.0 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can Gemma 4 26B A4B IT run on an GeForce RTX 4090?
Yes. Gemma 4 26B A4B IT fits on the GeForce RTX 4090 (24 GB) at Q6_K with an 8K context, using about 21.1 GiB, at an estimated 225 tokens/s.
Can I run Gemma 4 26B A4B IT on a Mac?
Yes. Gemma 4 26B A4B IT fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 50.1 GiB, at an estimated 70 tokens/s.
Can Gemma 4 26B A4B IT be used commercially?
Gemma 4 26B A4B 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-4-26B-A4B-it
- https://huggingface.co/google/gemma-4-26B-A4B-it/resolve/main/config.json
- https://huggingface.co/api/models/google/gemma-4-26B-A4B-it?expand[]=safetensors
- https://ollama.com/library/gemma4/tags