Platform
Private AI infrastructure — from compute to agents.
Target groups
For enterprise, SMBs, and individual developers.
Knowledge & Support
Everything you need to succeed with Mycelis.
Compute
Deploy open-source models on dedicated GPU hardware. Billed hourly, OpenAI-compatible endpoint, no shared resources.
Available GPUs
| GPU | VRAM | Suitable for | Price / hour |
|---|---|---|---|
| NVIDIA RTX 4090 | 24 GB | Llama 3.1 8B, Mistral 7B, Qwen 7B | €0.39 |
| NVIDIA RTX A6000 | 48 GB | Llama 3.1 70B (Q4), Mixtral 8x7B | €0.79 |
| NVIDIA A100 80GB | 80 GB | Llama 3.1 70B (FP16), 405B (Q4) | €1.99 |
| NVIDIA H100 SXM | 80 GB HBM3 | Llama 3.1 405B, training | On request |
All prices net, excluding VAT. Hourly billing, cancellable at any time.
Supported open-source models
Setup in 60 seconds
Select GPU type and model in the dashboard. Mycelis starts the instance automatically.
Create a personal access token (PAT) — takes less than 10 seconds.
Change base_url and api_key in your existing code. Done.
OpenAI-compatible endpoint
from openai import OpenAI
client = OpenAI(
base_url="https://api.mycelis.io/proxy/v1",
api_key="pat_..." # your personal access token
)
response = client.chat.completions.create(
model="llama-3.1-70b", # your deployment name
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)All GPU instances run on dedicated hardware — no shared resources, no data forwarding to third parties. Prompts and responses are not stored permanently. Data centers are in the EU. Full data ownership stays with the user.
Frequently asked questions
No credit card required. Free starter credits included.
Start for free