Run DiffusionGemma with Ollama
llama.cpp PR #24423 is still in draft. Ollama cannot bundle DiffusionGemma until the PR lands in llama.cpp mainline. Until then, the bridge workaround still works — using the PR branch of llama.cpp as a backend.
PR #24423 still in draft. Ollama native support blocked. Ecosystem status →
Workaround: Run DiffusionGemma Through Ollama Today
While waiting for official support, you can bridge DiffusionGemma into Ollama using a custom Modelfile that wraps the llama.cpp diffusion branch.
Step 1: Install Ollama
# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh
# Windows — download from https://ollama.com/download
# Or use WSL2 + Linux install above
Step 2: Build llama.cpp from PR Branch
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
# PR #24423 still in draft — must build from PR branch
git fetch origin pull/24423/head:diffusion-support
git checkout diffusion-support
mkdir build && cd build
cmake .. -DGGML_CUDA=ON # if you have NVIDIA GPU
make -j$(nproc)
Step 3: Download a GGUF Model
# Check /gguf/ for available GGUF downloads
# Place the model in Ollama's model directory
mkdir -p ~/.ollama/models/
# See our GGUF guide for download links
Step 4: Create a Modelfile
# Modelfile for DiffusionGemma
FROM ./diffusiongemma-26b-q4_k_m.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_predict 256
# Diffusion-specific parameters
PARAMETER diffusion_steps 8
PARAMETER block_size 256
TEMPLATE """{{ .Prompt }}"""
Step 5: Create and Run the Model
ollama create diffusiongemma -f Modelfile
ollama run diffusiongemma
Troubleshooting
"model format not supported" error
This means your GGUF file doesn't include the diffusion architecture flag. Make sure you're using a GGUF converted from the diffusion branch of llama.cpp, not the mainline release.
OOM (Out of Memory) on 16GB GPU
Use Q4_0 or Q4_K_M quantization. The 26B model at Q4 needs ~16GB VRAM. Close other GPU processes first.
Slow generation on CPU-only
DiffusionGemma's 4x speed advantage only applies with GPU acceleration. On CPU-only, performance will be comparable to or slower than autoregressive models of similar size.
Ollama says "model not found" after create
# Verify the model was created
ollama list
# If missing, check Modelfile path is correct
ollama create diffusiongemma -f ./Modelfile
PR #24423 In Draft — What's Needed for Native Ollama Support
llama.cpp PR #24423 is still in draft. The bridge workaround above uses the PR branch of llama.cpp. For true native support — a single ollama pull diffusiongemma — Ollama needs the PR to land in llama.cpp mainline first, then release an update bundling the merged engine.
| What | Status |
|---|---|
| llama.cpp PR #24423 | Draft ⚠️ |
| Ollama engine update | Pending — no ETA |
| Bridge workaround | Works today (uses PR branch) |
Once Ollama ships the update, this page will have native ollama pull instructions. Until then, the bridge above works with the llama.cpp PR branch. Track full ecosystem →
Got Ollama running? Here's how to keep it running.
This tutorial covers the bridge workaround. The Production Guide covers what's next: Docker Compose, GPU tuning, 20+ error fixes, multi-user serving — 8 chapters. $39 $29, lifetime updates.
Last updated: July 1, 2026