Module Review: Production

Congratulations on completing the Production module! You now understand how to take an LLM from a Jupyter notebook to a scalable, secure, and cost-effective production service.

1. Key Takeaways

  1. Continuous Batching: The single most important optimization for throughput. It eliminates “bubbles” in GPU utilization by inserting new requests immediately as others finish.
  2. PagedAttention: Solves memory fragmentation in the KV cache, allowing for much larger batch sizes.
  3. Quantization: Techniques like AWQ and GPTQ allow you to run massive models (70B) on consumer or single-node hardware by reducing precision to 4-bit.
  4. Guardrails: Essential for security. Use a “sandwich” architecture with deterministic (regex) and probabilistic (Llama Guard) checks on both input and output.
  5. Metrics: Track TTFT (latency) and TPS (throughput) separately. They trade off against each other.

2. Interactive Flashcards

What is PagedAttention?

A memory management algorithm (used in vLLM) that stores KV cache in non-contiguous blocks, virtually eliminating memory fragmentation.

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3. Cheat Sheet

Category Term Definition
Serving TTFT Time To First Token. Latency metric. Important for chat.
Serving Throughput Tokens generated per second across all users. Important for cost.
Serving vLLM High-performance serving engine known for PagedAttention.
Optimization Int4 / Int8 4-bit / 8-bit integer quantization formats.
Optimization KV Cache Stored attention states. Grows linearly with context length.
Optimization Flash Attention Algorithm to compute attention with minimal memory IO.
Safety Guardrails External validation layer wrapping the LLM call.
Safety Prompt Injection Hacking the model via input instructions.

4. Glossary

For a full list of terms, visit the Gen AI Glossary.

5. Next Steps

Now that you have mastered Production, you are ready to build real-world applications.