win-ai-local

name	win-ai-local
description	Local AI inference via Ollama — run LLMs on-device, manage models, detect NPU/GPU/DirectML hardware, zero cloud dependencies.
metadata	{"openclaw":{"emoji":"🧠","os":["win32"],"requires":{"bins":["ollama"]}}}

win-ai-local

Run LLMs locally on Windows via Ollama. Manage models, generate text, chat, and detect AI hardware (NPU, GPU, DirectML, WinML).

Requires Ollama installed. Works on Windows 10/11.

Detect AI Hardware

powershell.exe -NoProfile -Command "
Write-Host '--- NPU ---'
Get-CimInstance Win32_PnPEntity | Where-Object { $_.Name -match 'NPU|Neural|AI Boost|Movidius|VPU' } | ForEach-Object {
    Write-Host ('{0} [{1}]' -f $_.Name, $_.Status)
}
Write-Host ''
Write-Host '--- GPU ---'
Get-CimInstance Win32_VideoController | Where-Object { $_.Status -eq 'OK' } | ForEach-Object {
    Write-Host ('{0} (VRAM: {1} GB)' -f $_.Name, [math]::Round($_.AdapterRAM/1GB,1))
}
Write-Host ''
Write-Host '--- ML Runtimes ---'
$dml = Get-Item 'C:\Windows\System32\DirectML.dll' -ErrorAction SilentlyContinue
if ($dml) { Write-Host ('DirectML: {0}' -f $dml.VersionInfo.FileVersion) }
else { Write-Host 'DirectML: Not found' }
$winml = Get-Item 'C:\Windows\System32\windows.ai.machinelearning.dll' -ErrorAction SilentlyContinue
if ($winml) { Write-Host ('WinML: {0}' -f $winml.VersionInfo.FileVersion) }
else { Write-Host 'WinML: Not found' }
"

Ollama Status

powershell.exe -NoProfile -Command "
$proc = Get-Process -Name 'ollama*' -ErrorAction SilentlyContinue
if ($proc) {
    Write-Host ('Ollama: Running (PID {0})' -f ($proc.Id -join ', '))
} else {
    Write-Host 'Ollama: Not running'
}
$ver = & ollama --version 2>&1
Write-Host $ver
"

Start Ollama

powershell.exe -NoProfile -Command "
Start-Process ollama -ArgumentList 'serve' -WindowStyle Hidden
Write-Host 'Ollama server starting...'
Start-Sleep -Seconds 2
$proc = Get-Process -Name 'ollama*' -ErrorAction SilentlyContinue
if ($proc) { Write-Host 'Ollama is running.' }
else { Write-Host 'Failed to start. Try running ollama serve manually.' }
"

Stop Ollama

powershell.exe -NoProfile -Command "
Stop-Process -Name 'ollama*' -Force -ErrorAction SilentlyContinue
Write-Host 'Ollama stopped.'
"

List Models

powershell.exe -NoProfile -Command "
ollama list
"

Pull a Model

powershell.exe -NoProfile -Command "
ollama pull MODEL_NAME
"

Replace MODEL_NAME with the model to download (e.g., gemma3:4B, llama3.2:3b, phi4-mini, qwen2.5:7b).

Recommended models for AIPC (8-32 GB RAM):

Model	Size	Good for
gemma3:4B	3.3 GB	General, fast
phi4-mini	2.5 GB	Reasoning, compact
llama3.2:3b	2.0 GB	General, lightweight
qwen2.5:7b	4.7 GB	Multilingual (Chinese)
deepseek-r1:8b	4.9 GB	Coding, reasoning

Delete a Model

powershell.exe -NoProfile -Command "
ollama rm MODEL_NAME
"

Show Model Info

powershell.exe -NoProfile -Command "
ollama show MODEL_NAME
"

Generate Text

Single prompt, single response (no conversation):

powershell.exe -NoProfile -Command "
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
$body = @{
    model = 'MODEL_NAME'
    prompt = 'PROMPT_TEXT'
    stream = $false
} | ConvertTo-Json -Depth 3
$response = Invoke-RestMethod -Uri 'http://localhost:11434/api/generate' -Method POST -Body $body -ContentType 'application/json'
Write-Host $response.response
Write-Host ''
Write-Host ('Tokens: {0} | Duration: {1:N1}s | Speed: {2:N1} tok/s' -f $response.eval_count, ($response.total_duration / 1e9), ($response.eval_count / ($response.eval_duration / 1e9)))
"

• MODEL_NAME: model to use (e.g., gemma3:4B)
• PROMPT_TEXT: the prompt to send

With System Prompt

powershell.exe -NoProfile -Command "
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
$body = @{
    model = 'MODEL_NAME'
    prompt = 'PROMPT_TEXT'
    system = 'SYSTEM_PROMPT'
    stream = $false
} | ConvertTo-Json -Depth 3
$response = Invoke-RestMethod -Uri 'http://localhost:11434/api/generate' -Method POST -Body $body -ContentType 'application/json'
Write-Host $response.response
"

With Parameters (Temperature, Max Tokens)

powershell.exe -NoProfile -Command "
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
$body = @{
    model = 'MODEL_NAME'
    prompt = 'PROMPT_TEXT'
    stream = $false
    options = @{
        temperature = 0.7
        num_predict = 500
    }
} | ConvertTo-Json -Depth 3
$response = Invoke-RestMethod -Uri 'http://localhost:11434/api/generate' -Method POST -Body $body -ContentType 'application/json'
Write-Host $response.response
"

• temperature: 0.0 (deterministic) to 2.0 (creative), default 0.8
• num_predict: max tokens to generate, default 128

Chat (Multi-Turn)

powershell.exe -NoProfile -Command "
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
$body = @{
    model = 'MODEL_NAME'
    messages = @(
        @{ role = 'system'; content = 'SYSTEM_PROMPT' }
        @{ role = 'user'; content = 'USER_MESSAGE' }
    )
    stream = $false
} | ConvertTo-Json -Depth 5
$response = Invoke-RestMethod -Uri 'http://localhost:11434/api/chat' -Method POST -Body $body -ContentType 'application/json'
Write-Host $response.message.content
Write-Host ''
Write-Host ('Tokens: {0} | Speed: {1:N1} tok/s' -f $response.eval_count, ($response.eval_count / ($response.eval_duration / 1e9)))
"

Multi-Turn Conversation

powershell.exe -NoProfile -Command "
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
$body = @{
    model = 'MODEL_NAME'
    messages = @(
        @{ role = 'user'; content = 'FIRST_MESSAGE' }
        @{ role = 'assistant'; content = 'FIRST_RESPONSE' }
        @{ role = 'user'; content = 'SECOND_MESSAGE' }
    )
    stream = $false
} | ConvertTo-Json -Depth 5
$response = Invoke-RestMethod -Uri 'http://localhost:11434/api/chat' -Method POST -Body $body -ContentType 'application/json'
Write-Host $response.message.content
"

Embeddings

Generate text embeddings for semantic search or RAG:

powershell.exe -NoProfile -Command "
$body = @{
    model = 'MODEL_NAME'
    input = 'TEXT_TO_EMBED'
} | ConvertTo-Json -Depth 3
$response = Invoke-RestMethod -Uri 'http://localhost:11434/api/embed' -Method POST -Body $body -ContentType 'application/json'
Write-Host ('Dimensions: {0}' -f $response.embeddings[0].Count)
Write-Host ('First 5: {0}' -f ($response.embeddings[0][0..4] -join ', '))
"

Use an embedding model like nomic-embed-text or mxbai-embed-large.

Common Workflows

Quick local inference (no API key needed)

1. win-ai-local: check Ollama status (start if needed)
2. win-ai-local: generate text with a prompt
3. Use the result in your workflow

Translate text locally

1. win-ai-local: generate with system prompt "Translate to English"
2. No data leaves the machine — fully private

Summarize a document privately

1. Read the document content
2. win-ai-local: generate with prompt "Summarize: {content}"
3. Confidential data stays local

Agent delegates to local LLM

1. Main agent identifies a sub-task suitable for local inference
2. win-ai-local: chat with appropriate system prompt
3. Main agent uses the response
4. Saves API costs for simple tasks

On-device RAG pipeline

1. win-ai-local: generate embeddings for documents
2. Store embeddings locally
3. On query, embed the query and find similar docs
4. win-ai-local: chat with retrieved context

Notes

• Requires Ollama installed and running (ollama serve).
• Models are stored at %USERPROFILE%\.ollama\models\.
• Ollama API runs at http://localhost:11434 — fully local, no data leaves the machine.
• GPU acceleration: Ollama auto-detects and uses available GPU (Intel Arc, NVIDIA, AMD).
• NPU acceleration: not yet supported by Ollama directly. Use OpenVINO or WinML for NPU inference.
• For Chinese text, use qwen2.5 models which have strong multilingual support.
• Inference speed depends on model size and hardware. On Intel Arc 140V: ~5-10 tok/s for 4B models.
• stream = $false returns the full response at once. Set to $true for streaming (requires different parsing).
• Max context window varies by model (typically 2K-128K tokens). Check with ollama show MODEL_NAME.
• For production use, consider running Ollama as a Windows service for auto-start.