[FEATURE] LLM Quantization & Optimization — Deploy efficient models on edge devices

[gelluisaac]

Description

Optimize LLM deployment through quantization, distillation, and compression
techniques to enable local execution on edge devices and reduce API costs.

Scope

Build model optimization pipeline for efficient LLM deployment.

Files to Touch/Create

• astroml/llm/optimization/__init__.py
• astroml/llm/optimization/quantizer.py — Model quantization (GPTQ, AWQ, GGUF)
• astroml/llm/optimization/distiller.py — Knowledge distillation
• astroml/llm/optimization/compressor.py — Model compression
• astroml/llm/optimization/validator.py — Quality validation after optimization
• astroml/llm/optimization/registry.py — Optimized model registry
• astroml/models/optimized/ — Storage for optimized models
• configs/llm/optimization/ — Optimization configs

Optimization Techniques

1. Quantization: INT8, INT4, GPTQ, AWQ
2. Distillation: Small model trained on large model outputs
3. Pruning: Remove redundant weights
4. Speculative Decoding: Small model drafts, large model verifies

Implementation Details

• Use HuggingFace optimum and auto-gptq
• Benchmark quality vs speed tradeoffs
• Support Llama 2/3, Mistral, Phi-2
• Automated quality regression testing
• Model serving with llama.cpp or vLLM

Acceptance Criteria

• Quantized models achieve >90% of base model quality
• Inference speed improves >2x
• Model size reduced >75%
• Memory usage fits on consumer GPUs (8GB)
• Local deployment works without API calls
• Quality metrics tracked per optimization

Deployment Targets

• Local GPU servers (RTX 4090, A100)
• CPU inference (for development)
• Edge devices (Jetson, Raspberry Pi)
• Browser-based (WebAssembly)

Cost Impact

• Eliminate API costs for high-volume queries
• Reduce latency for simple tasks
• Enable offline operation

Labels

enhancement, llm, optimization, infrastructure