Getting Started

Quickstart

Deploy a live LLaMA 3 API endpoint in under 60 seconds.

1

Install the CLI

pip install bricqs

Requires Python 3.9+. The CLI talks directly to the BRICQS API over HTTPS.

2

Log in to your account

bricqs login

Your credentials are stored in ~/.bricqs/config.json with 600 permissions. Don't have an account? Sign up free →

3

Deploy LLaMA 3

bricqs deploy my-llama-api --model meta-llama/Llama-3-8B-Instruct

The CLI polls in real-time and prints the live endpoint once provisioning completes (~2–5 min for first GPU pull).

textDeploying my-llama-api
  Model:       meta-llama/Llama-3-8B-Instruct
  Environment: production
  Replicas:    0–1

████░░ waiting_for_model

✓ Running!

╭─ Deployment Live ───────────────────────────────────────────────╮
│ Endpoint: https://bricqs-my-llama-api.bricqs.run                │
│                                                                  │
│ Test it:                                                         │
│   curl .../v1/chat/completions -d '{"model":"llama3:8b",...}'   │
│                                                                  │
│ View logs:  bricqs logs <id>                                     │
│ Stop:       bricqs stop <id>                                     │
╰──────────────────────────────────────────────────────────────────╯
4

Call your model

Your endpoint is fully OpenAI-compatible. Use curl or the OpenAI SDK:

bashcurl https://<your-endpoint>/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3:8b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Or with the OpenAI Python SDK:

pythonfrom openai import OpenAI

client = OpenAI(
    base_url="https://<your-endpoint>/v1",
    api_key="not-needed",
)

response = client.chat.completions.create(
    model="llama3:8b",
    messages=[{"role": "user", "content": "What is quantum computing?"}],
)
print(response.choices[0].message.content)
5

Stop when done

bashbricqs stop <deployment-id>

# Or permanently delete:
bricqs delete <deployment-id>

Stopped deployments scale to zero — you're only billed while running.

What just happened?
  • BRICQS provisioned a GPU runtime container with a T4 GPU in westus2
  • The Ollama runtime pulled LLaMA 3 8B weights (~4.7 GB) into GPU memory
  • An OpenAI-compatible HTTPS endpoint went live with auto-scaling 0→2 replicas
  • Real-time metrics (CPU, memory, GPU %, requests, latency) are collected automatically
CLI Reference →View all models →