Project 10: The Local “Llama-in-a-Box” (Production Grade)

Build a local inference setup with quantization, caching, and monitoring for production-like use.

Quick Reference

Attribute Value
Difficulty Level 5: Expert
Time Estimate 3-4 weeks
Language Python
Prerequisites Quantization, inference basics
Key Topics deployment, caching, monitoring

1. Learning Objectives

By completing this project, you will:

  1. Package a local Llama model with quantization.
  2. Implement KV cache and batching.
  3. Add monitoring for latency and memory.
  4. Set up a production-like API service.
  5. Benchmark throughput and cost.

2. Theoretical Foundation

2.1 Local Production Inference

Running locally requires careful tuning of memory, latency, and stability.

3. Project Specification

3.1 What You Will Build

A local inference server that serves a quantized Llama model with monitoring and benchmarks.

3.2 Functional Requirements

  1. Quantized model with config presets.
  2. Inference server with batching.
  3. Monitoring for latency and memory.
  4. Benchmark suite for throughput.
  5. Config management for hardware profiles.

3.3 Non-Functional Requirements

  • Stable uptime for local runs.
  • Graceful OOM handling.
  • Clear performance reports.

4. Solution Architecture

4.1 Components

Component Responsibility
Model Loader Load quantized model
Server Handle requests
Cache KV cache + batching
Monitor Track metrics
Benchmark Measure throughput

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P10-llama-box/
├── src/
│   ├── load.py
│   ├── server.py
│   ├── cache.py
│   ├── monitor.py
│   └── benchmark.py

5.2 Implementation Phases

Phase 1: Model + server (8-12h)

  • Load quantized model and serve API.
  • Checkpoint: server handles requests.

Phase 2: Cache + batching (8-12h)

  • Add KV cache and batching.
  • Checkpoint: throughput improves.

Phase 3: Monitoring + benchmarks (8-12h)

  • Add metrics and benchmark suite.
  • Checkpoint: performance report generated.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit cache memory usage tracking
Integration server request handling
Regression benchmark stable throughput

6.2 Critical Test Cases

  1. Server survives OOM by rejecting large requests.
  2. Batching improves throughput.
  3. Monitoring reports correct metrics.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
OOM crashes server dies enforce limits
Slow inference high latency tune batch size
Missing metrics no visibility add monitoring hooks

8. Extensions & Challenges

Beginner

  • Add a simple UI client.
  • Add request logging.

Intermediate

  • Add autoscaling profiles.
  • Add model hot swapping.

Advanced

  • Add multi-model serving.
  • Compare with TGI or vLLM.

9. Real-World Connections

  • Local LLM deployments need production-like stability.
  • Cost optimization depends on batching and quantization.

10. Resources

  • Llama model docs
  • Inference server references

11. Self-Assessment Checklist

  • I can run a local quantized inference server.
  • I can measure latency and memory.
  • I can benchmark throughput.

12. Submission / Completion Criteria

Minimum Completion:

  • Local inference server + quantized model

Full Completion:

  • Monitoring + benchmark suite

Excellence:

  • Multi-model serving
  • Comparison with production frameworks

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY.md.