Use Case

GPU Cloud for AI Agent Developers

Build multi-agent systems without token limits. Deploy open-source models on dedicated GPUs and call them unlimited times for a fixed hourly cost.

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The Token Problem

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Multi-Agent systems consume thousands of tokens per Agent per conversation turn

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A 6-agent system (e.g. OpenClaw) burns 500K–2M tokens daily

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API costs scale linearly with Agent count — more agents = more cost

payments

Monthly API bills reach $500–$3,000 for production multi-agent apps

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The GPU Solution

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Rent one GPU, deploy an open-source model, call it unlimited times

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Fixed cost — doesn’t grow with request volume or Agent count

speed

Works with vLLM, Ollama, TGI, and all major inference frameworks

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Serve multiple Agents simultaneously from a single GPU endpoint

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Fully private — your data never passes through a third-party API

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OpenAI-compatible API — just change the base_url in your Agent code

Quick Start for Agent Developers

Go to Deploy, select a model

Pick DeepSeek-V3 for coding/reasoning, or Qwen3-8B for fast multi-agent workloads.

Open Deploy Page →

Click Deploy — your model is live in 60 seconds

We handle GPU allocation, environment setup, model download, and API server. You just wait.

Update your agent config

Add your endpoint to your agent's environment:

# .env OPENAI_API_BASE=https://deployment-xxxx-11434.550w.link/v1 OPENAI_API_KEY=ollama # any non-empty string works

That's it. Your agents now run on unlimited tokens. No API key. Fixed hourly cost.

Advanced: run multiple models on one GPU endpointexpand_more

One Ollama instance can hold several models. Use Pick Model on your instance row to pull additional ones, then switch between them by changing the model field in each request. Ollama will hot-swap them in and out of VRAM automatically (use keep_alive to keep the active one resident):

# Planning agent uses a smaller, faster model plan = client.chat.completions.create( model="qwen2.5:0.5b", messages=plan_messages, extra_body={"keep_alive": "5m"}, ) # Coding agent uses a larger, higher-quality model code = client.chat.completions.create( model="qwen2.5:14b", messages=code_messages, extra_body={"keep_alive": "30m"}, )

A single A100 40G can hold several 7B–14B models simultaneously, letting one GPU back a whole agent team.

Which GPU Should I Pick?

GPU	VRAM	Best For	Price
RTX 4090	24 GB	Single agent on 7B–8B models (Qwen-8B, Llama-3.1-8B)	$0.35/hr
A100 40G	40 GB	Multi-agent on 7B–14B, or single 30B quantized	$0.66/hr
L40 / L40S	48 GB	Production 30B full precision, high-throughput inference	$0.65 – $0.89/hr
A800 80G	80 GB	70B full precision, heavy multi-agent production workloads	$1.18/hr
H20 96G	96 GB	Long-context 70B+ reasoning, reasoning chains	$1.35/hr

Start Building

Get $5 free credits and deploy your first model in under 60 seconds.