LLM Response Time Optimizer

Optimizing Mistral-7B inference for 3x faster response times using JAX.

Overview

This project demonstrates production-level optimization of large language models by converting Mistral-7B from PyTorch to JAX and applying quantization, efficient caching, and JIT compilation. The goal is to achieve 2-3x speedup while maintaining output quality.

Goals

• Speed: Reduce response time from ~2.5s to <1s per query (2-3x speedup target, achieved 16x on GPT-2!)
• Memory: Decrease GPU memory usage from ~14GB (FP16) to ~7GB (INT8) via quantization
• Quality: Maintain 98%+ output similarity to original model
• Cost: Lower inference costs through faster generation and reduced memory

Tech Stack

Core Frameworks

• JAX: For automatic differentiation and XLA compilation
• Flax: Neural network library built on JAX
• Transformers: Hugging Face library for model loading

Optimization Libraries

• jax.jit: Just-in-time compilation for performance
• Optax: For any additional fine-tuning (optional)

Utilities

• datasets: For loading evaluation datasets (Alpaca)
• rouge-score: For output quality evaluation
• matplotlib/seaborn: For performance visualizations

Optimization Techniques

1. Model Conversion: PyTorch → JAX/Flax for XLA optimization
2. INT8 Quantization: 2x memory reduction on model weights (FP32 → INT8)
3. KV-Cache: Avoid redundant computation during generation
4. JIT Compilation: Fuse operations for faster execution
5. Batched Inference: Process multiple requests efficiently

Benchmarking

Evaluation on 1,000 instructions from the Alpaca dataset measuring:

• Tokens per second
• Latency (p50, p95, p99)
• Memory usage
• Output quality (ROUGE scores, exact match rate)
• Cost per 1M tokens

Current Results (GPT-2, Phase 4)

Metric	Non-Cached	Cached + JIT	Improvement
Tokens/sec	1.50	24.45	16.32x faster
Time (15 tokens)	10.03s	0.63s	16.32x faster
Model Memory (INT8)	163MB	163MB	Same (quantized)
KV-Cache Overhead	-	~38MB	Minimal
Output Match	Identical	Identical	Perfect
Quality	Correct text	Correct text	100%

Memory Note: INT8 quantization reduces GPT-2 from 326MB (FP32) to 163MB (INT8) = 2.00x reduction. KV-cache adds ~38MB overhead for storing attention keys/values.

Optimization Breakdown:

• KV-Cache + JIT combined: 16.32x speedup (measured)
• Note: Cannot measure KV-cache vs JIT separately with decorator approach

Status: Phase 4 (KV-Cache + JIT) COMPLETE for GPT-2!

• Achieved 16.32x speedup on GPT-2 (far exceeds 2-3x target!)
• Fixed critical bug in attention output projection
• Applied JIT compilation to 8 core functions with @jax.jit decorators
• Text generation quality verified (identical to PyTorch GPT-2)
• All benchmarks passing on GPT-2
• Performance measured after proper JIT warmup (all sequence lengths pre-compiled)

Important: JAX JIT compiles separately for each input shape. Warmup must generate at least as many tokens as the actual test to ensure all shapes are pre-compiled for accurate benchmarking.

Note: All optimizations tested and validated on GPT-2. Mistral-7B implementation pending.

Phase 4 Complete! Ready for Phase 5: Mistral-7B Implementation & Benchmarking.

Target Results (Mistral-7B)

Metric	Baseline (PyTorch FP16)	Target (JAX INT8)	Status
Tokens/sec	8-10	20-25 (2.5-3x faster)	⏳ Not Started
Latency	2.5s	<1s	⏳ Not Started
Memory	~14GB	~7GB (2x reduction)	⏳ Not Started
Quality	100%	98%+	⏳ Not Started

Note: Mistral-7B support not yet implemented. Based on GPT-2 results (16.32x speedup), we expect similar or better performance for Mistral-7B when implemented.

Project Structure

.
├── src/
│   ├── model_conversion.py      # PyTorch to JAX conversion
│   ├── quantization.py          # INT8 quantization
│   ├── cached_generation.py     # Optimized generation with KV-cache + JIT
│   └── kv_cache.py              # KV-cache utilities
├── tests/
│   ├── test_generation.py       # Generation quality tests
│   └── test_optimization_comparison.py  # Performance benchmarks
├── OPTIMIZATION_GUIDE.md        # Detailed optimization documentation
├── BENCHMARKING.md              # Benchmarking best practices
├── Execution_Plan.md            # Project roadmap and progress
└── README.md

Installation

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install jax[cuda12] flax transformers datasets rouge-score matplotlib seaborn