Moving from local experimentation to production deployment requires careful planning around infrastructure, performance, and reliability.

Inference Servers

• vLLM — The most popular production inference server. Supports continuous batching, PagedAttention for efficient memory use, and OpenAI-compatible API. Handles hundreds of concurrent requests.
• TGI (Text Generation Inference) — Hugging Face's inference server. Well-integrated with the HF ecosystem, supports quantization and tensor parallelism.
• TensorRT-LLM — NVIDIA's optimized inference stack. Maximum performance on NVIDIA GPUs but more complex to set up.

Cloud GPU Providers

• Replicate — Pay-per-prediction model hosting. Easiest to set up: push your model, get an API.
• Together AI — Managed inference for popular open-source models. OpenAI-compatible API.
• Modal — Serverless GPU compute. Great for bursty workloads with auto-scaling to zero.
• RunPod — GPU rentals with Docker support. Good for custom setups at competitive prices.
• AWS/GCP/Azure — Enterprise-grade GPU instances. Most expensive but fullest feature set.

Key Production Considerations

Scaling:

• Start with a single GPU and measure throughput (requests/second)
• Add GPUs horizontally for more concurrent requests
• Use load balancers to distribute across multiple inference servers
• Consider auto-scaling for variable workloads

Monitoring:

• Track latency (time-to-first-token and total generation time)
• Monitor GPU utilization and memory usage
• Log requests and responses for quality assurance
• Set up alerts for error rates and latency spikes

Cost Optimization:

• Use quantized models (Q4/Q8) to fit on smaller GPUs
• Implement request batching for higher throughput
• Cache common responses when appropriate
• Scale down during off-peak hours

Fine-Tuning for Your Domain

The real power of open-source models is fine-tuning:

• LoRA — Low-Rank Adaptation. Trains small adapter weights instead of the full model. Fast, cheap, and effective.
• QLoRA — LoRA on a quantized model. Fine-tune a 70B model on a single GPU.
• Full fine-tuning — Modify all weights. Best quality but requires significant compute.

Fine-tuning workflow: Collect domain-specific data → Format as instruction/response pairs → Train with LoRA → Evaluate → Deploy the adapter alongside the base model.

Well done completing this course! You now have a comprehensive understanding of the open-source AI ecosystem and how to deploy models for production use. 🛠️