Daniel Hiltgen a6293eb516 llm: allow iGPU mmproj offload with fit padding (#16996)
* llm: allow iGPU mmproj offload with fit padding

llama.cpp's fit pass sizes text-model placement before the multimodal projector is loaded. Ollama had been avoiding that risk on non-Metal iGPUs by disabling projector offload entirely, which forces CLIP onto CPU on GB10 and Strix Halo even when the projector has ample memory available.

Let integrated GPUs use the same projector-memory check as other GPUs. When projector offload is enabled, add the estimated projector memory plus the existing 1 GiB headroom to Ollama-owned LLAMA_ARG_FIT_TARGET so fit leaves space for the later projector allocation. If Ollama/device setup already supplied a fit target, add the projector pad to it. If the user set LLAMA_ARG_FIT_TARGET explicitly, leave it exactly as provided.

Fixes #16419

* review comments
2026-07-07 15:28:42 -07:00
2026-07-02 11:44:31 -07:00
2026-07-02 11:44:31 -07:00
2023-08-22 09:40:58 -07:00
2026-07-03 18:30:45 -07:00
2025-01-29 15:03:38 -08:00
2026-06-01 10:44:21 -07:00
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2026-07-06 12:52:15 -07:00
2024-08-01 17:06:06 -07:00
2026-07-06 13:31:22 -07:00

ollama

Ollama

Start building with open models.

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curl -fsSL https://ollama.com/install.sh | sh

or download manually

Windows

irm https://ollama.com/install.ps1 | iex

or download manually

Linux

curl -fsSL https://ollama.com/install.sh | sh

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The official Ollama Docker image ollama/ollama is available on Docker Hub.

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ollama

You'll be prompted to run a model or connect Ollama to your existing agents or applications such as Claude Code, OpenClaw, OpenCode , Codex, Copilot, and more.

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To launch a specific integration:

ollama launch claude

Supported integrations include Claude Code, Codex, Copilot CLI, Droid, and OpenCode.

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Use OpenClaw to turn Ollama into a personal AI assistant across WhatsApp, Telegram, Slack, Discord, and more:

ollama launch openclaw

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Run and chat with Gemma 4:

ollama run gemma4

See ollama.com/library for the full list.

See the quickstart guide for more details.

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Ollama has a REST API for running and managing models.

curl http://localhost:11434/api/chat -d '{
  "model": "gemma4",
  "messages": [{
    "role": "user",
    "content": "Why is the sky blue?"
  }],
  "stream": false
}'

See the API documentation for all endpoints.

Python

pip install ollama
from ollama import chat

response = chat(model='gemma4', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response.message.content)

JavaScript

npm i ollama
import ollama from "ollama";

const response = await ollama.chat({
  model: "gemma4",
  messages: [{ role: "user", content: "Why is the sky blue?" }],
});
console.log(response.message.content);

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