Gemma 4 26B A4B VRAM requirements: can you run it locally?

Gemma 4 26B A4B is from Google. Its Hugging Face repo was created on 2026-03-11, it is licensed Apache 2.0, and Hugging Face lists 25,805,936,206 parameters.^[1] The model card reports about 3.8B active parameters per token.^[1] Native context is up to 131,072 tokens where the card documents it.^[2]

Memory needed at each quantization

Quantization stores each weight in fewer bits. Smaller files fit on smaller GPUs, at some cost in quality. Below are GGUF builds from the bartowski (or listed) repo using exact Hugging Face file sizes (1 GB = 1,000,000,000 bytes), largest first.^[4]

Ollama's gemma4:26b tag is the usual one-line install for the 26B A4B build when the library lists it.^[5][6]

Context length adds memory (the KV cache)

Hybrid sliding-window (1,024 tokens) and global layers with shared K/V; we have not found a published per-token figure, so it is not yet sourced. Until we can publish a figure, the fit table below uses weights only.

KV cache not yet sourced for this model. Fit tables below use weights only until a published per-token figure is available.

Which GPUs and Macs it fits on

Each cell compares the weights (KV not yet sourced) with the device's memory.^{[7][8][9][10][11][12][13]}

KV cache not yet sourced for this hybrid-attention model; fit cells use weights only (no context), same rule as the compare table asterisk.

How to read it: "Fits" means weights use under 90% of memory. "Tight" means 90–100%. "No" means it won't fit on the GPU alone. The 90% line is our own rule of thumb, not a vendor figure. On a Mac, macOS also needs some of the unified memory, so treat Mac rows as optimistic.

How to run it

The easiest way is Ollama when the library carries this build: ollama run gemma4:26b.^[5] With llama.cpp or LM Studio, download a GGUF such as the Q4_K_M file from bartowski/google_gemma-4-26B-A4B-it-GGUF.^[4]

If the model is bigger than your VRAM, these tools can keep some layers in system RAM, but every token then waits on the slower memory, so expect much lower speed.

Cheapest GPU to rent for Gemma 4 26B A4B

On-demand Secure Cloud prices listed on Runpod's pricing page (confirm before you rent — prices move):^[14]

Prices checked on 2026-10-28. They change often, so confirm on the provider's page. Rent on Runpod Referral link

Or call it by API: compare LLM API prices.

See all GPU rental prices for more providers.

Frequently asked questions

How much VRAM does Gemma 4 26B A4B need at 4-bit?

About 17.0 GB for the Q4_K_M weights alone.^[4] Add KV cache once a sourced per-token figure is available.

Can I run it on a consumer GPU?

A 16–24 GB card is the practical target at Q4_K_M. Check the fit table above for your exact card.

Is BF16 practical?

BF16 is about 50.5 GB.^[4] Prefer Q4_K_M or Q8_0 unless you have the memory.

How we calculated

• Weights are the exact GGUF file sizes on Hugging Face (shards added together).^[4]
• KV cache is not yet sourced for this architecture; fit cells use weights only.
• Device memory is from NVIDIA and Apple spec pages.
• Not included: engine working buffers, the OS and display. Leave headroom.

Sources

All sources checked on 2026-10-28.

1. Hugging Face model page: google/gemma-4-26B-A4B-it (parameter count, license, repo date). https://huggingface.co/google/gemma-4-26B-A4B-it
2. Hugging Face model card / README for architecture and context. https://huggingface.co/google/gemma-4-26B-A4B-it
3. config.json (google/gemma-4-26B-A4B-it). https://huggingface.co/google/gemma-4-26B-A4B-it/blob/main/config.json
4. Hugging Face GGUF file sizes: bartowski/google_gemma-4-26B-A4B-it-GGUF. https://huggingface.co/bartowski/google_gemma-4-26B-A4B-it-GGUF/tree/main
5. Ollama library tags (where listed). https://ollama.com/library/gemma4
6. Ollama pull / tag sizes where published for this family.
7. NVIDIA GeForce RTX 4060 / 50-series 8 GB specs. https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/
8. NVIDIA GeForce RTX 3060 specs (12 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3060-3060ti/
9. NVIDIA GeForce RTX 4060 Ti / 16 GB class specs. https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4060-4060ti/
10. NVIDIA GeForce RTX 3090 specs (24 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
11. NVIDIA GeForce RTX 4090 specs (24 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
12. NVIDIA GeForce RTX 5090 / RTX 6000 Ada specs. https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
13. Apple MacBook Pro / Mac Studio unified memory specs. https://www.apple.com/macbook-pro/specs/
14. Runpod, GPU pricing. https://www.runpod.io/pricing