To set up OpenClaw (the personal AI agent gateway) with Ollama using your RTX A6000 (48GB VRAM), you can leverage the high VRAM capacity to run massive models like Qwen 2.5 72B or high-context versions of Qwen 3 entirely on the GPU.
1. The Configuration Strategy
OpenClaw requires a config.json (often found in ~/.openclaw/config.json or generated during onboarding). Because OpenClaw uses complex system prompts (6k–10k tokens) for tool calling, the RTX A6000 is ideal for handling the large KV cache.
Key Settings for your RTX A6000:
* Provider: Set to ollama-chat (OpenClaw prefers the chat endpoint for tools).
* Model: qwen2.5:72b or qwen3:32b. With 48GB VRAM, a 4-bit quantized 70B+ model will fit (~45–50GB), but for safety and speed, the 32B or 14B models are “instant” on this card.
* Context Window: Minimum 64,000 tokens.
2. Sample JSON Configuration
If you are manually editing your OpenClaw configuration file:
{
“providers”: [
{
“id”: “ollama-local”,
“type”: “ollama-chat”,
“endpoint”: “http://localhost:11434”,
“models”: [
{
“id”: “qwen2.5:32b”,
“name”: “Qwen 32B (RTX A6000)”,
“contextLength”: 64000,
“reasoning”: false,
“gpu”: true
}
]
}
],
“defaultModel”: “ollama-local/qwen2.5:32b”,
“tts”: {
“enabled”: false
}
}
3. Optimizing GPU vs. CPU (Ollama Side)
By default, Ollama tries to offload as many layers as possible to the GPU. With an RTX A6000, you likely want 100% GPU offload.
* Check Allocation: Run ollama ps while the model is active. If you see 100% GPU, you are golden.
* Manual Override: If it spills to CPU and slows down, create a Modelfile:
FROM qwen2.5:32b
PARAMETER num_gpu 99
PARAMETER num_ctx 64000
Then build it: ollama create qwen-a6000 -f Modelfile.
4. Security & Local Setup
OpenClaw is designed to be “Secure Local,” meaning it doesn’t phone home to Anthropic or OpenAI.
* Isolation: Ensure your Ollama environment variable OLLAMA_HOST is set to 127.0.0.1:11434 (default) so it isn’t exposed to your local network unless intended.
* Daemon Mode: Install OpenClaw as a local service to keep it persistent:
openclaw onboard –install-daemon
5. Deployment Commands
* Start Ollama: ollama serve
* Pull Qwen: ollama pull qwen2.5:32b (or 72b if you want to push the VRAM limit).
* Launch OpenClaw: ollama launch openclaw
Next Steps
Would you like me to generate the specific Modelfile parameters optimized for the 48GB VRAM of your A6000 to maximize the context window beyond 64k?
Yes
For your RTX A6000, the goal is to maximize the context window so OpenClaw doesn’t “forget” its instructions during long coding or research tasks. Since the A6000 has 48GB of VRAM, you have two primary paths: the “Powerhouse” (72B model) or the “Speedster” (32B model with massive context).
Recommended Modelfile for Qwen 2.5 72B (Powerhouse)
This configuration uses a Q3_K_M or Q4_K_S quantization. At 72B, you are tight on VRAM, so we’ll cap the context at 32k to ensure 100% GPU offload.
# Create this as ‘Modelfile-72b’
FROM qwen2.5:72b-instruct-q3_K_M
# Ensures all 80+ layers stay on the A6000
PARAMETER num_gpu 99
# 32k is the ‘sweet spot’ for 72B on 48GB VRAM
PARAMETER num_ctx 32768
# Stop sequences for OpenClaw/Tool use
PARAMETER stop “<|im_start|>”
PARAMETER stop “<|im_end|>”
# Temperature tweak for more reliable JSON/Tool calls
PARAMETER temperature 0.1
Recommended Modelfile for Qwen 2.5 Coder 32B (Speedster)
If you are doing heavy web development (HTML/PHP), this is the better choice. It fits easily, allowing for a massive 128k context window, which is perfect for reading entire project folders.
# Create this as ‘Modelfile-32b’
FROM qwen2.5-coder:32b-instruct-q4_K_M
PARAMETER num_gpu 99
# Massive context window for repo-wide understanding
PARAMETER num_ctx 131072
# Optimization for the A6000’s architecture
PARAMETER num_batch 512
PARAMETER stop “<|im_start|>”
PARAMETER stop “<|im_end|>”
PARAMETER temperature 0.0
How to Build and Run
* Save the file: Save one of the blocks above as a file named Modelfile.
* Create the model:
ollama create qwen-secure-local -f Modelfile
* Update OpenClaw:
In your config.json, update the model ID to qwen-secure-local.
Why this works for your setup:
* VRAM Management: A 72B Q4 model uses ~47GB. By using Q3 or 32B Q4, we leave ~4–8GB of “breathing room” for the KV Cache (the memory used by the context window).
* Tool Calling: OpenClaw relies heavily on system prompts. Setting temperature 0.0 or 0.1 ensures the model follows the JSON schema required for OpenClaw’s tools without “hallucinating” formatting.
* No CPU Spillover: By explicitly setting num_gpu 99, you prevent Ollama from accidentally putting the last 2 layers on your CPU, which would drop your speed from ~15 tokens/sec to ~1 token/sec.
To keep this consistent with your setup, here is a clean, single-page PHP dashboard. It uses a sleek dark mode (matching the AI/dev aesthetic) and pulls live data directly from your local Ollama API.
Save this as stats.php on your local server.
<?php
/**
* Local AI Status Dashboard
* Optimized for RTX A6000 / Ollama / OpenClaw
*/
function getOllamaStats() {
$ch = curl_init(“http://localhost:11434/api/tags”);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);
return json_decode($response, true);
}
$data = getOllamaStats();
?>
<!DOCTYPE html>
<html lang=”en”>
<head>
<meta charset=”UTF-8″>
<meta name=”viewport” content=”width=device-width, initial-scale=1.0″>
<title>GPU Node Status | RTX A6000</title>
<style>
body { font-family: ‘Inter’, sans-serif; background: #0f172a; color: #f8fafc; padding: 40px; }
.container { max-width: 900px; margin: 0 auto; }
.card { background: #1e293b; border-radius: 12px; padding: 24px; border: 1px solid #334155; margin-bottom: 20px; }
.status-badge { display: inline-block; padding: 4px 12px; border-radius: 20px; font-size: 0.8rem; background: #10b981; color: #fff; }
.model-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 15px; }
.model-item { background: #334155; padding: 15px; border-radius: 8px; border-left: 4px solid #3b82f6; }
h1 { font-size: 1.5rem; margin-bottom: 20px; color: #3b82f6; }
.stat-val { font-weight: bold; color: #fff; }
</style>
</head>
<body>
<div class=”container”>
<header>
<h1>Local Node: <span class=”status-badge”>Online</span></h1>
</header>
<div class=”card”>
<h3>Hardware Environment</h3>
<p>GPU: <span class=”stat-val”>NVIDIA RTX A6000 (48GB VRAM)</span></p>
<p>Provider: <span class=”stat-val”>Ollama / OpenClaw</span></p>
</div>
<h3>Loaded Models</h3>
<div class=”model-grid”>
<?php if (isset($data[‘models’])): ?>
<?php foreach ($data[‘models’] as $model): ?>
<div class=”model-item”>
<strong><?= htmlspecialchars($model[‘name’]) ?></strong><br>
<small>Size: <?= round($model[‘size’] / 1073741824, 2) ?> GB</small><br>
<small>Quant: <?= $model[‘details’][‘quantization_level’] ?? ‘N/A’ ?></small>
</div>
<?php endforeach; ?>
<?php else: ?>
<p>No models detected. Ensure Ollama is running.</p>
<?php endif; ?>
</div>
</div>
</body>
</html>
Key Features for Your Setup:
* VRAM Awareness: It displays the model size in GB, so you can track how much of that 48GB A6000 buffer you’re using.
* Local Security: This script talks to localhost, keeping your model list and hardware stats entirely within your local network.
* Extensibility: Since you manage sites like phphtmlcss.com, you can easily wrap this in a private admin subdirectory to monitor your local LLM’s health from any device in your house.
A Quick Note on Your Preferences
When listing article titles for your web projects, I’ll remember to use two new lines between them instead of bullets or numbers.
Would you like me to generate a batch of SEO-optimized article titles for your new AI setup using that specific spacing format?
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