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[Qudsiaamir]

Efficiently Serving LLMs

Production-oriented examples for understanding, implementing, and benchmarking
core LLM inference serving techniques.

Why this project matters

LLM serving performance is shaped by practical engineering choices: greedy
decode loops, KV-cache usage, static and continuous batching, quantization,
adapter routing, and benchmark discipline. This repository turns notebook-heavy
experiments into a clean Python project that demonstrates how those mechanics
can be organized, tested, documented, and evaluated like production software.

Key features

• Reusable Python package for LLM serving primitives under src/
• Extracted helpers for token generation, batching, quantization, LoRA, and multi-LoRA
• Preserved exploratory notebooks with cleaned repository placement
• Local benchmark harness with JSON output support
• Pytest test suite for package imports, request utilities, benchmark summaries, and quantization
• Dockerfile, Makefile, dependency metadata, and GitHub Actions CI
• Architecture, benchmarking, troubleshooting, and design-decision documentation

Architecture overview

The project separates exploratory notebooks from reusable code:

• efficient_llm_serving.generation: greedy next-token generation and KV-cache decode helpers
• efficient_llm_serving.batching: batch decode utilities and request objects for continuous batching simulations
• efficient_llm_serving.quantization: educational uint8 affine quantization helpers
• efficient_llm_serving.lora: toy LoRA and multi-LoRA model components
• efficient_llm_serving.benchmarking: latency timing and summary utilities
• benchmarks/run_benchmark.py: local benchmark runner for adapter-serving scenarios

See docs/architecture.md for component and data-flow details.

Tech stack

• Python 3.10+
• PyTorch and Hugging Face Transformers for model execution
• pytest for tests
• Ruff for linting and formatting
• Docker for reproducible container execution
• GitHub Actions for CI

The package imports without PyTorch installed. Tensor/model functionality raises
clear installation guidance at runtime.

Repository structure

.
├── benchmarks/              # Local benchmark runner and results template
├── docs/                    # Architecture, design, benchmarking, troubleshooting
├── examples/                # Runnable examples
├── notebooks/exploratory/   # Preserved notebook experiments
├── src/efficient_llm_serving/
├── tests/
├── Dockerfile
├── Makefile
├── pyproject.toml
└── README.md

Quickstart

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,ml]"
make test

For a lighter install without PyTorch or Transformers:

pip install -e ".[dev]"

Configuration

Runtime examples use command-line arguments. Environment variables are not
required for the local toy benchmarks. If you connect to hosted model APIs, copy
.env.example and provide your own endpoint and API key.

Usage examples

Run a small Hugging Face decode example:

python examples/generate_text.py \
  --model distilgpt2 \
  --prompt "Efficient LLM serving matters because" \
  --max-new-tokens 16

Use the package directly:

from efficient_llm_serving.benchmarking import summarize_latencies

summary = summarize_latencies([0.012, 0.015, 0.014])
print(summary.p50_s)

Benchmarking

Run the local multi-LoRA benchmark:

make benchmark

Or write JSON results:

python benchmarks/run_benchmark.py \
  --scenario multi-lora-gathered \
  --samples 25 \
  --batch-sizes 1 2 4 8 16 \
  --output benchmarks/local_results.json

No benchmark results are claimed in this repository. Use
benchmarks/results_template.md and
docs/benchmarking.md to record measurements generated
locally on your target hardware.

Testing

make test

Some tests require PyTorch and are skipped automatically when it is unavailable.

Docker usage

Build and run the test container:

docker build -t efficiently-serving-llms .
docker run --rm efficiently-serving-llms

CI/CD

The GitHub Actions workflow installs the package with development extras, runs
Ruff, and executes pytest. ML-heavy benchmark jobs are intentionally left for
local or GPU-enabled runners.

Professional highlights

• Clean Python package structure extracted from notebook experiments
• Reproducible development workflow with Makefile, Dockerfile, and CI
• Benchmark-ready framework that avoids invented performance claims
• Modular implementation of LLM serving concepts: KV-cache decoding, batching, quantization, LoRA, and multi-LoRA
• Documentation written for technical reviewers, recruiters, and engineering managers
• Clear separation between exploratory research artifacts and reusable production-style code

Roadmap

• Add GPU benchmark profiles for A10/T4/L4/A100-class hardware
• Add FastAPI inference endpoint for batch and streaming generation demos
• Add OpenTelemetry metrics hooks for latency, throughput, and request queue depth
• Add Kubernetes deployment manifests for an inference service demo
• Add richer notebook smoke tests with Papermill once model dependencies are available

Limitations

• Quantization and LoRA modules are educational implementations, not replacements
  for production inference engines.
• Benchmark outputs depend heavily on hardware, installed libraries, and model
  choice; generate results locally before making performance claims.
• The preserved notebooks originated as lesson experiments and may require
  additional local dependencies for full execution.

License

MIT. See LICENSE.