oktoblast

<p align="center"> <img src="./assets/okto_logo.png" alt="OktoEngine Banner" width="50%" /> </p> <p align="center"> <img src="./assets/okto_logo2.png" alt="OktoScript Banner" width="50%" /> </p> <h1 align="center">OktoBLAS</h1> <p align="center"> <strong>🏆 Beats PyTorch by up to 21% • Fused Attention 3.8x Faster 🏆</strong> </p> <p align="center"> <a href="https://pypi.org/project/oktoblas/"><img src="https://img.shields.io/pypi/v/oktoblas?color=blue&label=PyPI" alt="PyPI"></a> <a href="https://www.oktoseek.com/"><img src="https://img.shields.io/badge/OktoSeek-Official-orange" alt="OktoSeek"></a> <a href="#license"><img src="https://img.shields.io/badge/License-Proprietary-red" alt="License"></a> </p>

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🔥 Performance

FP16 GEMM

Fused Attention

📊 Benchmarks on NVIDIA RTX 4070 Laptop GPU

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What is OktoBLAS?

OktoBLAS is a proprietary, high-performance BLAS engine developed by OktoSeek. It is the core computational backbone of OktoEngine, our native AI training platform.

Built 100% from scratch with zero dependency on NVIDIA cuBLAS.

🎯 Key Highlights

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🌱 Energy Savings & Environmental Impact

OktoBLAS helps save energy and reduce CO₂ emissions worldwide.

By running AI workloads 12% faster, OktoBLAS reduces GPU power consumption significantly:

🌍 Impact for Humanity

Every GPU-hour saved means:

• Less electricity consumed from power plants
• Less CO₂ emissions into the atmosphere
• Lower costs for AI research and development
• More accessible AI for everyone

📖 Full Enterprise Savings Analysis →

This is why OktoSeek created OktoBLAS — not just for performance, but for a sustainable AI future.

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🔬 OktoSeek Research Mission

One of OktoSeek's primary research areas is developing new mathematical techniques and optimization methods that reduce AI training time without compromising model quality.

Why This Matters for Humanity

┌─────────────────────────────────────────────────────────────────────┐
│                  THE PROBLEM WE'RE SOLVING                          │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│   Today, training a large AI model costs:                           │
│                                                                     │
│   💰 $100,000 to $10,000,000+ in compute                            │
│   ⚡ 1,000,000+ kWh of electricity                                   │
│   🕐 Weeks to months of GPU time                                    │
│   🌍 Tons of CO₂ emissions                                          │
│                                                                     │
│   This means only big companies can create AI.                      │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘

OktoSeek's Solution

By making training faster and cheaper, we enable:

The Vision

"We believe AI should be accessible to everyone — not just those who can afford million-dollar GPU clusters. By making training 12%+ faster with the same hardware, we're democratizing AI creation and building a more sustainable future."

— OktoSeek Research Team

Faster training means:

• ✅ More people can create AI
• ✅ More innovations in less time
• ✅ Lower barriers to entry
• ✅ Smaller environmental footprint

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🔧 Architecture

OktoBLAS is the computational core of the OktoSeek platform:

OktoScript → OktoEngine → OktoBLAS → GPU (Tensor Cores)

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📦 Python Package

OktoBLAS is available as a standalone Python package.

Installation

pip install oktoblas

Quick Start

import oktoblas as ob
import numpy as np

# FP16 Matrix Multiplication (Tensor Cores)
A = np.random.randn(2048, 2048).astype(np.float16)
B = np.random.randn(2048, 2048).astype(np.float16)
C = ob.matmul_fp16(A, B)  # 40+ TFLOPS

# Fused Attention (3x faster)
Q = np.random.randn(4, 512, 64).astype(np.float32)
K = np.random.randn(4, 512, 64).astype(np.float32)
V = np.random.randn(4, 512, 64).astype(np.float32)
output = ob.attention(Q, K, V)

# Library info
ob.info()

API Reference

# GEMM Operations
ob.matmul(A, B)           # FP32 matrix multiplication
ob.matmul_fp16(A, B)      # FP16 with Tensor Cores

# Fused Operations
ob.attention(Q, K, V)     # Fused Q×K^T×V attention

# Utilities
ob.info()                 # Library information
ob.is_cuda_available()    # Check GPU availability
ob.get_device_info()      # GPU details
ob.benchmark(op, size)    # Run benchmarks

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🚀 Maximum Performance Guide

For best results with OktoBLAS:

1. Enable cuDNN benchmark
2. Use FP16 and Tensor Cores
3. Enable automatic mixed precision (AMP)

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🧪 OktoScript Integration

Within OktoEngine, OktoBLAS is configured through OktoScript v1.3+:

# okto_version: "1.3"

PROJECT "my-ai-model"

# Enable OktoBLAS as BLAS backend
BLAS {
    backend: "oktoblas"
    precision: "fp16"
}

# Accelerate operations with OktoBLAS
ACCELERATE {
    gemm: "oktoblas"
    attention: "oktoblas"
    fused_ops: true
}

# Enable Tensor Cores
TENSOR_CORES {
    enabled: true
    precision: "fp16"
}

MODEL {
    base: "gpt2"
    device: "cuda"
}

TRAIN {
    epochs: 3
    batch_size: 16
    mixed_precision: true
}

# Performance optimization
OPTIMIZE {
    cudnn_benchmark: true
    tf32: true
}

Run Training

# Standard training
okto train -f train.okt

# With verbose performance logging
okto train -f train.okt --verbose --show-tflops

Expected Output

[OktoBLAS] Device: NVIDIA RTX 4070
[OktoBLAS] FP16 GEMM: 40.6 TFLOPS (beats PyTorch!)

Step   100 | Loss: 2.45 | Speed: 520 ex/s | TFLOPS: 40.2
Step   200 | Loss: 1.89 | Speed: 518 ex/s | TFLOPS: 39.9
...
Training complete! Average: 515 ex/s

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🌐 OktoSeek Ecosystem

OktoBLAS is a core component of the OktoSeek AI platform — a complete ecosystem for building, training, and deploying AI models with maximum efficiency.

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📁 Examples

• examples/python/ — Python usage examples
• docs/ENTERPRISE_SAVINGS.md — Energy & Cost Savings

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📜 License

OktoBLAS Binary License — Proprietary

Free for personal and commercial use. Redistribution and modification of binaries prohibited.

Copyright © 2025 OktoSeek AI. All Rights Reserved.

See LICENSE for full terms.

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🔗 Links

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<p align="center"> <strong>🏆 OktoBLAS — The First Independent BLAS to Beat PyTorch 🏆</strong> </p> <p align="center"> Made with precision by <a href="https://www.oktoseek.com"><strong>OktoSeek AI</strong></a> </p>