Zenith Performance Suite

High-Performance Inference Compiler for PyTorch

+69% Inference Speedup | -87% Energy Consumption | 0.000 MSE

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What is Zenith?

Zenith is a high-performance inference compiler that integrates seamlessly with PyTorch via torch.compile(). It optimizes your models for faster inference while maintaining perfect numerical precision.

import torch
import zenith  # Registers the backend automatically

model = YourModel()
optimized_model = torch.compile(model, backend="zenith")

# 1.7x faster inference with identical outputs
output = optimized_model(input_tensor)

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Quick Start

Installation

pip install pyzenith

Requirements

• Python 3.10+
• PyTorch 2.0+
• CUDA 11.8+ (for GPU acceleration)

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Benchmark Results (v0.3.0)

All benchmarks were conducted on Google Colab with a Tesla T4 GPU (15GB VRAM).

Hero Metrics

Metric	PyTorch	Zenith	Improvement
Inference Speed	15.37 tok/s	26.02 tok/s	+69.21%
Energy Consumption	49.34 J	6.09 J	-87.66%
Numerical Precision	Baseline	0.000000 MSE	PERFECT

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Scientific Validation Suite

Zenith has been rigorously tested across multiple dimensions to ensure production readiness:

1. Inference Performance

Text generation throughput comparison using TinyLlama-1.1B.

Metric	PyTorch	Zenith	Delta
Throughput (TPS)	15.37	26.02	+69.21%

View Full Log

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2. Numerical Precision

Bitwise accuracy verification across critical operations.

Operation	Max Difference	Status
Softmax	0.000000000	PASSED
LayerNorm	0.000000000	PASSED
GELU	0.000000000	PASSED

View Precision Log

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3. Dynamic Shape Adaptability

Variable-length input handling without recompilation overhead.

Metric	Result
Recompilation Events	None Detected
Latency Scaling	Linear

View Dynamic Shape Log

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4. Energy Efficiency

Power consumption measurement for sustainable AI.

Metric	PyTorch	Zenith	Savings
Duration	1.85s	0.12s	15x Faster
Energy	49.34 J	6.09 J	-87.66%

View Energy Log

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5. Numerical Stability

Determinism verification over 500 inference iterations.

Metric	PyTorch	Zenith
Max Drift	0.000000000	0.000000000
Determinism	100%	100%

View Stability Log

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Final Verdict

Aspect	Status	Notes
Inference Speed	EXCEPTIONAL	+69% over PyTorch
Numerical Precision	PERFECT	Zero divergence
Dynamic Shapes	GOOD	No recompile overhead
Energy Efficiency	EXCEPTIONAL	-87% consumption
Stability	PERFECT	100% deterministic

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Repository Structure

Zenith-Lab/
    Production_RealWorld_Tests/     # Real benchmark notebooks
    Legacy_Dummy_Tests/             # Historical test files
    assets/
        HASIL_COLAB/
            Production_RealWorld_Tests/   # Detailed result logs
            Legacy_Dummy_Tests/           # Historical logs
        *.png                             # Benchmark charts
    Zenith_Scientific_*.ipynb       # Scientific validation notebooks
    README.md
    LICENSE

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Run Your Own Benchmarks

Open any notebook directly in Google Colab:

Test	Colab Link
Real-World Benchmark	Open
Precision Test	Open
Dynamic Shape Test	Open
Energy Test	Open
Stability Test	Open

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Community Sandbox Lab

This repository is your safe space to experiment with Zenith.

We understand that adopting a new compiler in production can be risky. That's why we've opened this repo as a public testing ground. Use it to:

• Test Zenith on your own models before committing to it
• Benchmark against your specific workloads
• Explore edge cases and limitations
• Learn how Zenith works under the hood

How to Contribute Your Experiments

If your experiment produces measurable, reproducible results, we encourage you to share it with the community. Your contribution becomes learning material for others who are also evaluating Zenith.

Contribution Guidelines:

1. Fork this repository
2. Create your experiment in a new notebook (e.g., experiments/your_model_benchmark.ipynb)
3. Document your methodology clearly (model, dataset, hardware, metrics)
4. Include raw output logs as evidence
5. Submit a Pull Request with a summary of your findings

What We Accept:

• Successful benchmarks (speedup, energy savings, etc.)
• Edge case discoveries (what works, what doesn't)
• Comparisons with other frameworks (TensorRT, ONNX Runtime, etc.)

What We Expect:

• Honesty in reporting (both wins and losses)
• Reproducible experiments (others should be able to verify)
• No promotional content or spam

Your contribution helps the entire community make informed decisions. Think of it as "sedekah ilmu" - sharing knowledge for the benefit of others.

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Contributing

For bug fixes, documentation improvements, or new test cases, please:

1. Open an issue first to discuss your proposal
2. Fork the repository
3. Create a feature branch
4. Submit a Pull Request

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License

MIT License - Copyright (c) 2025 Wahyu Ardiansyah

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_{Built with precision. Validated with science. Ready for production.}