NVIDIA B200 on Koyeb

Overview

The NVIDIA B200 Tensor Core GPU is NVIDIA’s next-generation Blackwell-architecture accelerator designed for the most demanding AI, large language model (LLM), and high-performance computing (HPC) workloads. Unlike single-GPU products, the B200 is a data center-class GPU optimized for extreme compute density and scale-out performance, providing unmatched memory and bandwidth for large-scale model training and inference.

Key characteristics of the B200 include:

• Massive GPU memory (≈180–192 GB HBM3e) with very high memory bandwidth (~8 TB/s), enabling large models and datasets to be processed efficiently.
• Next-generation Blackwell Tensor Cores with support for low-precision formats (including FP8 and FP4), accelerating AI training and inference.
• Designed for SXM data center form factors and high-speed interconnects (e.g., NVLink) to scale across multi-GPU clusters.
• Ideal for integration into large systems (such as DGX B200) that aggregate multiple B200 GPUs for extreme AI performance.

The B200 pushes performance beyond prior generations (including H100 and H200) by combining a large memory footprint with ultra-high bandwidth and next-generation tensor compute density, making it a future-proof choice for cutting-edge AI infrastructure.

Best-Suited Workloads

The B200 excels in large-scale, high-throughput AI and HPC workloads — especially those that benefit from very large memory and bandwidth:

• LLM Training at Scale
  Train massive transformer models (100B+ parameters) with reduced need for model sharding or complex parallelism, thanks to large HBM3e capacity and high throughput.

• High-Performance LLM Inference
  Serve inference for very large models or multi-tenant endpoints with high throughput and low latency, especially when paired with optimized precision formats (e.g., FP8/FP4).

• Generative AI at Enterprise Scale
  Power generative text, image, or multimodal AI services that require sustained throughput and large working memory.

• Large-Scale AI Clusters and Distributed Training
  When deployed in multi-GPU clusters with high-speed NVLink and network fabrics, B200 excels in horizontal scaling for training and inference.

• Memory-Intensive HPC Workloads
  Accelerate scientific simulations, fluid dynamics, optimization, and data analytics that benefit from large memory and high interconnect bandwidth.

Why Deploy on Koyeb?

Deploying B200-based workloads on Koyeb’s serverless GPU platform provides a powerful combination of performance, scalability, and ease of use:

• Elastic, On-Demand GPU Scaling
  Provision B200 instances as needed for training, inference, or mixed workloads — without owning or managing underlying hardware.

• Cost-Efficient High-Performance Compute
  Access ultra-high-end GPU capabilities without long-term capital expenditure, paying only for the compute you consume.

• Unified Training and Serving Platform
  Train large models and serve them on the same cloud platform with integrated deployment tooling — from experimentation to production.

• Global Low-Latency Deployment
  Serve inference closer to users and data sources, improving responsiveness for real-time AI services.

• Enterprise-Ready Platform Features
  Combine B200 performance with Koyeb’s orchestration, autoscaling, monitoring, and reliability to deploy and manage AI services at scale.

The NVIDIA B200 on Koyeb is ideal for teams and enterprises that need maximum memory footprint, ultra-high memory bandwidth, and next-generation tensor performance for the most demanding AI and HPC workloads.