Local LLM Interview Path

Master local LLM deployment and optimization with real-world use cases using open source tools. Each scenario includes key topics, interview questions, and technical concepts you'll encounter at top tech companies.

Use Cases

50+

Interview Questions

Setting Up Local LLM with Ollama

IntermediateGetting Started

Deploy and manage local LLMs using Ollama for development and production use cases.

🎯 Key Topics to Master:

✓Ollama Installation & Configuration

✓Model Library & Model Selection

✓Modelfile Customization

✓REST API Integration

✓System Prompt Configuration

✓Resource Management (CPU/GPU)

💡 Common Interview Questions:

1.What are the advantages of running LLMs locally?
2.How does Ollama simplify local LLM deployment?
3.What hardware requirements are needed for different model sizes?
4.How do you customize model behavior with Modelfiles?
5.What are the tradeoffs of local vs cloud LLMs?

🔧 Technical Concepts:

GGUF model formatContext window configurationTemperature and sampling parametersModel quantization levels (Q4, Q5, Q8)GPU offloading strategies

📉

Model Quantization Techniques

AdvancedOptimization

Optimize model size and inference speed through quantization while maintaining quality.

🎯 Key Topics to Master:

✓Quantization Fundamentals (FP16, INT8, INT4)

✓GPTQ and AWQ Quantization

✓GGUF Format and llama.cpp

✓Post-Training Quantization (PTQ)

✓Quantization-Aware Training (QAT)

✓Accuracy vs Performance Tradeoffs

💡 Common Interview Questions:

1.What is model quantization and why is it important?
2.What are the differences between GPTQ, AWQ, and GGUF?
3.How much quality loss occurs with different quantization levels?
4.When should you use 4-bit vs 8-bit quantization?
5.How does quantization affect model performance?

🔧 Technical Concepts:

Weight-only vs activation quantizationK-quants in GGUFCalibration datasetsMixed precision inferencePerplexity evaluation

⚡

High-Performance Inference with vLLM

AdvancedInference Optimization

Deploy production-grade LLM inference servers with vLLM for maximum throughput and efficiency.

🎯 Key Topics to Master:

✓PagedAttention Algorithm

✓Continuous Batching

✓KV Cache Management

✓Tensor Parallelism

✓OpenAI-Compatible API

✓Benchmarking & Performance Tuning

💡 Common Interview Questions:

1.How does vLLM achieve higher throughput than naive implementations?
2.What is PagedAttention and why is it effective?
3.How does continuous batching improve GPU utilization?
4.What are the memory requirements for different model sizes?
5.How do you scale vLLM horizontally?

🔧 Technical Concepts:

KV cache optimizationDynamic batching strategiesGPU memory fragmentationRequest scheduling algorithmsThroughput vs latency tradeoffs

💻

Running LLMs with LM Studio

IntermediateDesktop Deployment

Use LM Studio for easy local model deployment with a user-friendly interface and API server.

🎯 Key Topics to Master:

✓LM Studio Setup & Configuration

✓Model Discovery & Download

✓Local Server Mode

✓Chat Interface & Playground

✓Model Comparison Tools

✓Prompt Templates & Presets

💡 Common Interview Questions:

1.What are the benefits of LM Studio for development?
2.How do you compare different models effectively?
3.What role does LM Studio play in prototyping?
4.How do you export configurations for production?
5.What limitations exist with desktop LLM tools?

🔧 Technical Concepts:

HuggingFace model integrationLocal API server endpointsGPU acceleration supportPrompt template formatsModel switching and hot-loading

🔧

llama.cpp for Cross-Platform Deployment

AdvancedLow-Level Optimization

Deploy LLMs efficiently across different hardware using llama.cpp and its ecosystem.

🎯 Key Topics to Master:

✓llama.cpp Architecture

✓GGUF Model Format

✓CPU Optimization (AVX2, NEON)

✓Metal, CUDA, and ROCm Support

✓Memory Mapping & Quantization

✓Server Mode & API

💡 Common Interview Questions:

1.Why is llama.cpp popular for local deployment?
2.How does llama.cpp optimize for different CPUs?
3.What is the GGUF format advantage over GGML?
4.How do you choose between CPU and GPU inference?
5.What are the performance characteristics?

🔧 Technical Concepts:

SIMD instructions optimizationModel sharding across devicesFlash Attention implementationMmap for efficient loadingRope scaling for context extension

📚

Building a Local RAG System with Ollama

AdvancedRAG Integration

Combine local LLMs with RAG using Ollama, local embeddings, and vector databases.

🎯 Key Topics to Master:

✓Local Embedding Models (nomic-embed-text)

✓ChromaDB for Local Vector Storage

✓LangChain with Ollama Integration

✓Privacy-Preserving RAG

✓Document Processing Pipeline

✓End-to-End Local Architecture

💡 Common Interview Questions:

1.How do you build a fully local RAG system?
2.What are privacy benefits of local RAG?
3.How do you handle performance with local embeddings?
4.What vector databases work well locally?
5.How do you optimize for limited resources?

🔧 Technical Concepts:

Local embedding generationFAISS vs ChromaDB for local useBatch processing strategiesContext window managementHybrid CPU/GPU workload distribution

🎯

Fine-Tuning Local Models with LoRA

AdvancedModel Customization

Fine-tune open source models locally using parameter-efficient techniques like LoRA and QLoRA.

🎯 Key Topics to Master:

✓LoRA and QLoRA Principles

✓Training with Hugging Face PEFT

✓Dataset Preparation & Augmentation

✓Training on Consumer GPUs

✓Merging LoRA Adapters

✓Evaluation & Benchmarking

💡 Common Interview Questions:

1.What makes LoRA parameter-efficient?
2.How much VRAM is needed for fine-tuning?
3.What is the difference between LoRA and full fine-tuning?
4.How do you prepare training data?
5.What are best practices for evaluation?

🔧 Technical Concepts:

Adapter rank and alpha parametersQLoRA 4-bit quantizationGradient checkpointingLearning rate schedulingCatastrophic forgetting prevention

🎭

Multi-Model Orchestration

AdvancedArchitecture

Design systems that orchestrate multiple specialized local models for different tasks.

🎯 Key Topics to Master:

✓Model Routing Strategies

✓Specialized Model Selection

✓Task Classification

✓Load Balancing & Scaling

✓Fallback Mechanisms

✓Cost-Performance Optimization

💡 Common Interview Questions:

1.When should you use multiple specialized models?
2.How do you route requests to appropriate models?
3.What are strategies for model warm-up?
4.How do you balance cost and quality?
5.What monitoring is needed for multi-model systems?

🔧 Technical Concepts:

Model registry patternsRequest classificationModel pooling and reuseDynamic model loadingPerformance monitoring

📱

Edge Deployment and Mobile LLMs

AdvancedEdge Computing

Deploy lightweight LLMs on edge devices and mobile platforms for offline AI applications.

🎯 Key Topics to Master:

✓Model Compression Techniques

✓MLC LLM for Mobile

✓WebLLM for Browsers

✓TinyLlama and Small Models

✓On-Device Inference Optimization

✓Battery and Memory Constraints

💡 Common Interview Questions:

1.What challenges exist for mobile LLM deployment?
2.How do you optimize models for edge devices?
3.What is the smallest viable LLM size?
4.How do you handle limited compute on mobile?
5.What are use cases for edge LLMs?

🔧 Technical Concepts:

WebAssembly for browser inferenceNeural network pruningKnowledge distillationINT4 and binary quantizationProgressive loading strategies

📊

Benchmarking and Performance Optimization

AdvancedPerformance

Measure, analyze, and optimize local LLM performance across different hardware and configurations.

🎯 Key Topics to Master:

✓Latency vs Throughput Metrics

✓Token Generation Speed

✓Memory Profiling & Optimization

✓Batch Size Tuning

✓Hardware Benchmarking

✓Quality Evaluation (Perplexity, BLEU)

💡 Common Interview Questions:

1.How do you measure LLM inference performance?
2.What is tokens per second and why does it matter?
3.How do you identify bottlenecks?
4.What hardware gives best price/performance?
5.How do you balance quality and speed?

🔧 Technical Concepts:

Time to first token (TTFT)Inter-token latencyMemory bandwidth utilizationProfiling tools (nvprof, perf)Benchmark datasets (MMLU, HellaSwag)

📚 How to Use This Path

1. Study Each Use Case

Go through each scenario systematically. Understand local deployment strategies, optimization techniques, and hardware considerations.

2. Practice Interview Questions

Prepare answers for each question. Focus on explaining tradeoffs between different tools, quantization methods, and deployment options.

3. Build Local LLM Projects

Deploy models locally using Ollama, vLLM, or llama.cpp. Experiment with quantization and optimization techniques.

4. Benchmark and Optimize

Measure performance across different hardware. Learn to optimize for your specific use case and constraints.