Gemma 3 27B IT
Gemma 3 27B IT by Google DeepMind (27B parameters). At Q4_K_M with an 8K context it needs about 17.5 GiB of memory. Google's 27B with image input; 52 of its 62 layers use a 1,024-token sliding window, which keeps the KV cache small. It fits a 20–24 GB card at 4-bit.
Last updated: 2026-10-13
Model facts
Run it
ollama run gemma3:27b
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 | 17.2 GiB | 17.5 GiB | 19.4 GiB |
| Q5_K_M | 20.0 GiB | 20.3 GiB | 22.2 GiB |
| Q6_K | 23.0 GiB | 23.3 GiB | 25.2 GiB |
| Q8_0 | 29.3 GiB | 29.6 GiB | 31.5 GiB |
| FP16 | 54.0 GiB | 54.3 GiB | 56.2 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 3 27B IT need?
With an 8K context, about 17.5 GiB at Q4_K_M and 29.6 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can Gemma 3 27B IT run on an GeForce RTX 4090?
Yes. Gemma 3 27B IT fits on the GeForce RTX 4090 (24 GB) at Q6_K with an 8K context, using about 23.3 GiB, at an estimated 32 tokens/s.
Can I run Gemma 3 27B IT on a Mac?
Yes. Gemma 3 27B IT fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 54.3 GiB, at an estimated 9.9 tokens/s.
Can Gemma 3 27B IT be used commercially?
Gemma 3 27B IT is released under Gemma Terms of Use. The licence allows commercial use (read its conditions).
Sources
- https://ai.google.dev/gemma/docs/releases
- https://ai.google.dev/gemma/terms
- https://ai.google.dev/gemma/docs/core/model_card_3
- https://huggingface.co/unsloth/gemma-3-27b-it/resolve/main/config.json
- https://huggingface.co/google/gemma-3-27b-it
- https://ollama.com/library/gemma3:27b/blobs/3eb75691d2db
- https://huggingface.co/api/models/google/gemma-3-27b-it?expand[]=safetensors
- https://ollama.com/library/gemma3/tags