Meta Interview: Open-Source LLM Inference at Scale (EXPLAINED)
Hard Meta company question — Llama serving, GPU clustering, quantization, batching, and open-weight ops.
TL;DR — Quick Answer
Stack: model server (vLLM/TGI) with continuous batching, tensor parallel multi-GPU, INT4/FP8 quantization, speculative decoding, request routing by sequence length, autoscaling GPU pools, centralized model artifact registry, A/B model versions, and observability (tokens/sec, queue depth, GPU util). Optimize KV cache memory; use PD separation for prefill/decode at extreme scale; CDN-like edge for embeddings; fallback queues on overload. Open-source ops mean you own security patches and weight distribution compliance.
The Interview Question
Meta-style interview: How would you serve open-source LLMs (Llama class) at scale for billions of requests?
Deep Explanation
Sign in to unlock full answer
Get deep explanations, PDF export & all LLMs questions
- 14 more sections of deep explanation
- Real-world examples
- Common mistakes
- Interviewer expectations
- Follow-up questions
MetaLlamaInferencevLLMScaleMeta