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OktoBLAS

🏆 Beats PyTorch by up to 21% • Fused Attention 3.8x Faster 🏆

🔥 Performance

FP16 GEMM

Matrix Size	OktoBLAS	PyTorch	Result
1024×1024	33.9 TFLOPS	30.0 TFLOPS	+13.1% 🔥
2048×2048	40.6 TFLOPS	33.7 TFLOPS	+20.6% 🔥🔥
4096×4096	42.1 TFLOPS	40.1 TFLOPS	+5.0% ✅

Fused Attention

Configuration	OktoBLAS	PyTorch	Speedup
B4 S256 D64	1.06 TFLOPS	0.28 TFLOPS	3.8x 🔥
B4 S512 D64	1.20 TFLOPS	0.93 TFLOPS	1.3x ✅
B8 S256 D64	1.17 TFLOPS	0.55 TFLOPS	2.1x ✅

📊 Benchmarks on NVIDIA RTX 4070 Laptop GPU

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


100% Independent	No cuBLAS dependency
Beats PyTorch	Up to +21% faster 🔥
Fused Attention	Up to 3.8x faster 🔥
Production Ready	Powers OktoEngine

🌱 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:

Scale	GPUs	Annual Energy Saved	CO₂ Reduced	Cost Saved
Startup	1-4	400-1,700 kWh	160-680 kg	$60-$260
SMB	8-32	2,300-12,000 kWh	0.9-4.8 ton	$350-$1,800
Enterprise	64-256	27,000-107,000 kWh	11-43 ton	$4,000-$16,000
Hyperscaler	1024+	680,000+ kWh	272+ ton	$102,000+

🌍 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.

🔬 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:

Benefit	Impact
🧑‍🔬 Researchers	More experiments in less time
🏫 Universities	Train models on limited budgets
🚀 Startups	Compete with big tech companies
🌍 Developing Nations	Access to AI creation, not just consumption
🌱 Planet Earth	Less energy = less carbon emissions

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

🔧 Architecture

OktoBLAS is the computational core of the OktoSeek platform:

OktoScript → OktoEngine → OktoBLAS → GPU (Tensor Cores)

📦 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

🚀 Maximum Performance Guide

For best results with OktoBLAS:

Enable cuDNN benchmark
Use FP16 and Tensor Cores
Enable automatic mixed precision (AMP)

🧪 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

🌐 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.

Component	Description	Status
OktoScript	The AI Programming Language — DSL for model training	⭐ Popular
OktoEngine	Native AI Training Runtime — powered by OktoBLAS	Production
OktoBLAS	High-Performance BLAS — Beats PyTorch by 21%!	PyPI
OkTensor	GPU Tensor Library	Production
OktoStudio	AI Development IDE	Coming Soon

📁 Examples

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

📜 License

OktoBLAS Binary License — Proprietary

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

See LICENSE for full terms.

🔗 Links


Website	oktoseek.com
PyPI	pypi.org/project/oktoblas
GitHub	github.com/oktoseek
Twitter	@oktoseek

🏆 OktoBLAS — The First Independent BLAS to Beat PyTorch 🏆

Made with precision by OktoSeek AI

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