01What it can do
- Apache 2.0 (free for commercial use)
- Diffusion architecture (alternative to AR Transformers)
- Vision-language multimodal
- 128k native context
- —No official Ollama tag — install via HuggingFace
- —Diffusion architecture: less mature runtimes/quantization
- —Public benchmarks still limited
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for DiffusionGemma 26B-A4B Instruct?
To run DiffusionGemma 26B-A4B Instruct locally with Q4 quantization, you need about 15 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.
On the go: DiffusionGemma 26B-A4B Instruct also runs on a RTX laptop PC (16 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.