Meta LLM Inference Optimization Interview: Quantization, KV Cache, and Inference Acceleration

Background

I have 2 years of experience in inference optimization. Previously, I worked at an AI chip company responsible for model deployment and performance optimization, primarily doing quantization compression and inference acceleration for CV models. After LLMs took off and my company started deploying them, I naturally transitioned to LLM inference optimization. Zhipu AI's GLM series is among the most solid domestic LLMs, and when they were hiring inference optimization engineers, I applied without hesitation — this is my strongest area.

Interview Process Recap

Round 1: Model Compression + Quantization (approx. 1.5 hours)

The first interviewer was an engineer working on inference frameworks. He started by asking me to introduce quantization background.

The first question went straight to the core: What are the principles of INT8 quantization? What methods exist? I started with symmetric and asymmetric quantization, then introduced the differences between Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). The interviewer followed up on the pros and cons of symmetric vs. asymmetric quantization. I explained that symmetric quantization is simpler to implement but may have greater accuracy loss, while asymmetric quantization has better accuracy but slightly more complex computation. The interviewer added that hardware-level overhead of asymmetric quantization should also be considered.

Next was a practical question: What's the principle behind LLM.int8()? How does it differ from regular INT8 quantization? I detailed the mixed-precision decomposition method — using FP16 for outlier features and INT8 for the rest, saving memory while maintaining accuracy. The interviewer asked how outliers are detected, and I described threshold-based methods. He nodded.

Then came a newer topic: What's the principle behind GPTQ? I explained starting from the OBQ (Optimal Brain Quantization) approximation, covering layer-wise quantization, Hessian matrix approximation, and handling inter-layer dependencies. The interviewer followed up on the difference between GPTQ and AWQ. I explained that AWQ is activation-aware weight quantization focusing on important weight channels, while GPTQ is Hessian-based optimal quantization. The interviewer confirmed my understanding was correct.

They also asked about INT4 quantization challenges and solutions, and I covered severe accuracy degradation, group-wise quantization, and double quantization techniques. The final open-ended question: If model accuracy drops significantly after quantization, how would you troubleshoot and fix it? I suggested layer-by-layer accuracy analysis, adjusting quantization granularity, mixed-precision strategies, and QAT fine-tuning.

Round 2: KV Cache + Inference Acceleration (approx. 2 hours)

The second interviewer was a senior engineer working on inference engines, and the questions went very deep.

Opening question: What's the principle behind KV Cache? Why does it accelerate inference? I explained starting from the autoregressive generation process — each new token requires recomputing all previous KV pairs, and with caching, you only need to compute the new token's KV and concatenate. The interviewer followed up: How do you calculate KV Cache memory usage? I wrote the formula: 2 × num_layers × batch_size × seq_len × hidden_dim × sizeof(dtype). The interviewer confirmed it was correct.

Then the key topic: What KV Cache optimization methods exist? I listed several directions: MQA/GQA (multi-head shared KV), PagedAttention (paged management), KV Cache quantization, and Sliding Window Attention. The interviewer was particularly interested in PagedAttention and asked me to detail vLLM's PagedAttention implementation. I explained how it borrows from operating system virtual memory management, dividing KV Cache into fixed-size blocks allocated on demand, solving memory fragmentation.

The inference acceleration section was also extensive: What are vLLM's architecture and core optimizations? I covered Continuous Batching, PagedAttention, and Prefix Caching. The interviewer followed up on the difference between Continuous Batching and Static Batching, and I explained request-level vs. batch-level scheduling and how Continuous Batching improves GPU utilization.

They also asked about TensorRT-LLM vs. vLLM comparison. I analyzed them across performance, flexibility, and ease of use. TensorRT-LLM is stronger in raw inference performance, but vLLM is more flexible and easier to deploy. The interviewer followed up on TensorRT-LLM's kernel fusion optimizations, and I covered operator fusion, CUDA Graph, and FP8 support.

The final system design question: Design a high-throughput LLM inference service supporting streaming output and concurrent requests. I designed a solution covering load balancing, request scheduling, KV Cache management, and streaming. The interviewer said the architecture was reasonable but reminded me about prefix caching optimization for sharing across multiple requests.

Round 3: Deep Project Dive + Deployment Architecture (approx. 1.5 hours)

The third round was with the technical director, who valued engineering experience and systems thinking more.

He first asked me to describe my previous model deployment project, drilling into details: What model was deployed? What framework? What QPS? What P99 latency? How was monitoring done? I answered each one and shared a pitfall we encountered: long-sequence requests causing KV Cache overflow, which we solved through dynamic batching and request prioritization.

Then came an architecture question: How do you design the deployment architecture for an LLM inference service? I covered model loading, request routing, inference engine, and result return modules. The interviewer was particularly interested in load balancing for multi-GPU inference, and I discussed request allocation strategies under TP/PP modes and how to select optimal GPU combinations based on request length.

They also asked about how to do gradual rollouts for model version updates. I covered A/B testing, traffic switching, and rollback strategies. The interviewer thought the approach was mature.

The final interesting question: If you were to design a multi-model inference platform, how would you approach it? I discussed a unified model interface, resource scheduler, auto-scaling, and metering/billing as core modules. The interviewer said the direction was right but noted many implementation details to consider, like heterogeneous GPU requirements for different models.

Interview Questions Summary

1. INT8 quantization principles and methods (symmetric/asymmetric, PTQ/QAT)

2. LLM.int8() mixed-precision decomposition principle

3. GPTQ principles and differences from AWQ

4. INT4 quantization challenges and solutions

5. Troubleshooting and fixing accuracy degradation after quantization

6. KV Cache principles and memory usage calculation

7. KV Cache optimization methods (MQA/GQA/PagedAttention/quantization)

8. vLLM's PagedAttention implementation principles

9. Difference between Continuous Batching and Static Batching

10. TensorRT-LLM vs. vLLM comparison

11. TensorRT-LLM's kernel fusion optimizations

12. Design a high-throughput LLM inference service

13. LLM inference service deployment architecture design

14. Load balancing for multi-GPU inference

15. Multi-model inference platform design

Key Takeaways

1. Quantization is fundamental to inference optimization: You must understand INT8/INT4 quantization principles, methods, and applicable scenarios. Stay current with new methods like GPTQ and AWQ — interviewers value awareness of cutting-edge techniques.

2. KV Cache is the core of LLM inference: Don't just know it exists — understand memory usage calculation and optimization methods, especially PagedAttention and GQA, which are high-frequency interview topics.

3. Compare and understand inference engines: vLLM and TensorRT-LLM are the two most mainstream options. Know their pros, cons, and applicable scenarios — interviewers will likely ask for comparisons.

4. System design capability matters: Inference optimization isn't just algorithms. Interviewers also assess deployment architecture, load balancing, and gradual rollout strategies.

5. Real deployment experience is essential: There's a big gap between theory and practice. Interviewers will drill into QPS, latency, and monitoring metrics — it's hard to answer well without hands-on experience.

FAQ

Q: How much CUDA programming is required?
A: Rounds 1 and 2 didn't directly ask about CUDA, but understanding it helps with inference optimization. Round 3 asked some GPU-related questions where CUDA knowledge is a plus.

Q: Do I need to know Zhipu GLM's technical details?
A: No internal details needed, but knowing the basic architecture and characteristics of the GLM series is helpful, like GLM-4's inference performance.

Q: Is there on-site coding?
A: Round 1 had formula writing and pseudocode, Round 2 had architecture diagrams, and Round 3 was mainly system design discussion. No complete code writing required.

Q: Are hardware knowledge requirements strict?
A: Basic GPU architecture knowledge is needed — concepts like memory bandwidth, compute capability, and A100/H100 characteristics.

Q: How long is the interview process?
A: From application to completing Round 3 took about three weeks, with 4-5 days between rounds. The pace was moderate.