oktoblas 1.0.4

High-Performance BLAS Library by OktoSeek - Tensor Core GEMM and Fused Attention

OktoBLAS

The Independent BLAS Engine Powering OktoEngine

---

What is OktoBLAS?

OktoBLAS is a proprietary, high-performance Basic Linear Algebra Subprograms (BLAS) engine developed by OktoSeek. It is the core computational backbone of OktoEngine, our native AI training and inference platform.

Unlike wrapper libraries, OktoBLAS is built entirely from scratch using Rust and hand-tuned CUDA PTX assembly — with zero dependency on NVIDIA cuBLAS.

🎯 Key Highlights

100% Independent	No cuBLAS, no external BLAS dependencies
Hand-Tuned PTX	Every kernel optimized at assembly level
Tensor Core Native	Built for NVIDIA Tensor Cores (WMMA)
Production Ready	Powers OktoEngine in production
Python Available	Also released as standalone Python package

---

🏆 Performance

All benchmarks on NVIDIA RTX 4070 Laptop GPU using CUDA Events.

FP16 GEMM (Tensor Cores)

Matrix Size	OktoBLAS	PyTorch	Performance
1024×1024	29.1 TF	23.3 TF	125% ✓
2048×2048	35.1 TF	34.6 TF	101% ✓
4096×4096	36.5 TF	38.9 TF	94%

Fused Attention

Config	OktoBLAS	PyTorch	Speedup
B4 S256 D64	0.96 TF	0.28 TF	3.4x
B4 S512 D64	1.22 TF	0.93 TF	1.3x

---

📦 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)  # 35+ 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()

Output

============================================================
OktoBLAS by OktoSeek
High-Performance BLAS Library
============================================================
Version: 1.0.2
License: Proprietary (c) 2025 OktoSeek AI
Backend: CUDA PTX (Tensor Cores)

Features:
  - FP16/FP32 GEMM with Tensor Cores
  - Fused Attention kernel
  - 100% Independent (no cuBLAS)

https://www.oktoseek.com
============================================================

---

🔥 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.benchmark(op, size)    # Run benchmarks

---

🧪 OktoScript Integration

Within OktoEngine, OktoBLAS is configured through OktoScript:

BLAS {
    backend: "oktoblas"
    precision: "fp16"
}

ACCELERATE {
    gemm: "oktoblas"
    attention: "oktoblas"
}

TENSOR_CORES {
    enabled: true
}

---

🌐 OktoSeek Ecosystem

OktoBLAS is a core component of OktoSeek AI:

Component	Description
OktoScript	AI programming language
OktoEngine	Native AI training runtime
OktoBLAS	High-performance BLAS engine
OkTensor	GPU tensor library
OktoStudio	AI development IDE

---

📜 License

Proprietary License — Free for personal and commercial use.

Copyright © 2025 OktoSeek AI. All Rights Reserved.

---

🔗 Links

• Website: oktoseek.com
• GitHub: github.com/oktoseek
• PyPI: pypi.org/project/oktoblas

---

OktoBLAS — The BLAS engine built for AI