Ollama · Blocked on llama.cpp #24423 Draft

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.

WhatStatus
llama.cpp PR #24423Draft ⚠️
Ollama engine updatePending — no ETA
Bridge workaroundWorks 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 →

View PR #24423 on GitHub →

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.

Get the Production Manual — $29 →

Last updated: July 1, 2026