Amplify GPU Computing Velocity at Scale

AI training-focused high-performance computing infrastructure, fully optimized

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  • NVIDIA
  • TensorFlow
  • PyTorch
  • Microsoft
  • AWS
  • Intel

Dedicated Cloud for AI Training & Real-Time Inference Acceleration

Our cloud platform, built with researchers and developers in mind, provides high-performance GPU infrastructure, featuring optimized workflows and scalable resources engineered for AI model development and deployment.

  • Cutting-Edge GPU Hardware

    Get your hands on the most advanced GPU technology, with configurations optimized for AI and ML workloads

  • Simplified Deployment

    Launch models swiftly via our user-friendly interface and pre-configured environments for top frameworks

  • Specialized Support & Optimization

    Our ML engineers deliver tailored assistance to optimize your models and workflows

Jack BrightonSince 2025

"ByteCompute's Accelerated computing has reached the tipping point — general purpose computing has run out of steam. We need another way of doing computing — so that we can continue to scale so that we can continue to drive down the cost of computing, so that we can continue to consume more and more computing while being sustainable. Accelerated computing is a dramatic speed up over general-purpose computing, in every single industry while maintaining cost-effectiveness for our startup."

-Jack Brighton

GPU Clusters, Enterprise-Grade

Tailored to meet your unique computational requirements

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Platform for Architectural Computing

Platform Architecture
  • Multi-GPU Workload Distribution Layer

    Optimized for peak throughput as part of our distributed computing framework

  • Smart Resource Management

    Computing resources allocated dynamically to match your workload requirements

  • High-Speed Interconnect

    Ultra-fast inter-node links with minimal latency, enabling efficient parallel processing

Backed by Industry Leaders

"Switching to ByteCompute's accelerated computing platform has enabled us to manage complex data analytics workloads more effectively. It not only boosts the speed of data processing but also cuts down on the overall infrastructure costs, which is crucial for our business's growth and competitiveness in the market."
Data Science Lead, A Leading E-commerce Company
"Leveraging ByteCompute's GPU-enabled services, we've been able to scale our machine learning models to serve billions of user interactions per day. The infrastructure ensures high-performance and reliability, allowing us to deliver top-notch user experiences without sacrificing on the efficiency of our operations."
CTO, A Global Social Media Platform

Custom Model Training: Expert AI Solutions You Can Trust

Custom Model Training

Our team of AI specialists delivers end-to-end support across your implementation journey—from initial setup to performance fine-tuning.

  • Implementation guidance customized to your unique use case
  • Recommendations for optimizing performance
  • Custom architecture design to meet specialized requirements
  • Continuous technical support and maintenance services
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Optimized for ByteCompute.ai

  • Built specifically for large language models
  • Fine-tuned to handle computer vision workloads
  • Specialized setups crafted for generative AI
  • Built for ML Operations Excellence

    Our infrastructure is specifically designed to manage the specialized demands of modern machine learning workflows, from early development through to live production deployment.

  • Seamless Scaling Power

    Expand your computational resources with ease to match changing requirements, complete with automatic scaling support tied to workload demands.

  • Enterprise-Level Security Framework

    All-encompassing security measures, from network isolation to encrypted data storage and precise access controls, protect your AI assets effectively.

Hardware Specifications

  • A100 Cluster Specifications

    40GB or 80GB HBM2e memory per GPU

    Up to 2,000 GB/s memory bandwidth

    Up to 19.5 TFLOPS FP64 performance

    312 Tensor Cores, 6,912 CUDA Cores

  • H100 Cluster Specifications

    80GB HBM3 memory per GPU

    Up to 3,000 GB/s memory bandwidth

    Up to 26 TFLOPS FP64 performance

    528 Tensor Cores, 16,896 CUDA Cores

  • CPU & Memory Specifications

    AMD EPYC or Intel Xeon processors

    Up to 1TB RAM per node

    PCIe Gen4 interconnect

    Custom core allocation options

  • Storage Specifications

    Up to 64TB NVMe local storage

    Petabyte-scale network storage

    200 Gbps dedicated storage networking

    Automatic data replication