Generative AI & LLMs
LLM Inference Optimization
8 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- You want to quantize an LLM to cut serving cost. How do you decide how far to quantize, and how do you make sure you haven't degraded quality?
- A stakeholder wants to cut the GPU memory and serving cost of a self-hosted LLM by quantizing it to a lower precision. How do you reason about whether that's the right lever, and what could go wrong?Go Pro
- When an LLM generates a response, why is the first token often much slower than the tokens that follow, and what is the KV cache doing?Go Pro
- You're self-hosting an open-weight LLM behind an API and the service is too slow and too expensive under load. Walk me through how you'd raise throughput and cut latency without retraining or swapping the model.Go Pro
- A stakeholder read that speculative decoding can roughly double LLM generation speed for free and wants you to turn it on for your self-hosted serving stack. How do you reason about whether it'll actually help your workload, and where it can backfire?Go Pro
- Your LLM-backed feature is too slow and too expensive at the volume you're now serving. What are the main levers to bring latency and cost down, and what does each one trade off?Go Pro
- An LLM feature works but feels slow — users wait too long for a response. What are the levers you'd consider to make inference faster or cheaper?Go Pro
- What is quantization in the context of serving an LLM, and what's the trade-off you're making when you use it?Go Pro
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