Tired of wrestling with complex AI model deployments? In this episode, we dive into a game-changing approach to deploying DeepSeek R1—a ChatGPT-level reasoning model—securely and efficiently using Google Cloud Run with Nvidia L4 GPU support. This setup isn’t just experimental; it’s production-ready, scalable, and cost-optimised.
Here’s why this matters:
🔹 Production-Grade Simplicity: Skip the DevOps headache. Learn how to package DeepSeek R1 into a 5GB Docker container with Ollama, deploy via Cloud Run, and handle cold starts in just 4–6 seconds.
🔹 GPU Auto-Scaling: Instances scale dynamically with workload, eliminating idle costs.
🔹 Security & Privacy: Your data stays entirely within your cloud environment—no internet access required.
We’ll break down the key insights from the original video, including:
✅ Design & Deployment: Why separate application backends from model APIs and step-by-step packaging using Ollama and Cloud Build.
✅ Real-World Demo: See it in action!
✅ Performance & Scalability: Test cases, optimisation attempts, and outcomes.
✅ Cost Analysis: Is it cheaper than ChatGPT?
✅ Key Benefits: Why this setup is a game-changer for AI deployments.
This setup stands out because it:
👉 Scales to Zero: Pay nothing when idle—ideal for internal tools or bursty workloads.
👉 Enterprise-Ready: Perfect for B2B/B2C apps requiring privacy, compliance, and low latency.
👉 Future-Proof: Easily swap DeepSeek R1 for other open-source models without rearchitecting.
If you want to dive deeper, check out the original video for more details: https://www.youtube.com/watch?v=7H6fJVf79o0
Who should listen?
💡 Engineers streamlining AI deployments.
💡 Teams building secure, internal LLM tools.
💡 Cloud architects optimising cost-performance trade-offs.
Let’s discuss: Have you tried GPU-backed Cloud Run? How are you balancing open-source models with production demands? Share your thoughts!
This podcast description was generated by AI based on the original video. For the full experience, including visuals and detailed demonstrations, visit the original video linked above.
