Elevate Your AI Workloads: AMD Instinct™ MI325X GPU Droplets are Now Available on DigitalOcean

Elevate Your AI Workloads: AMD Instinct™ MI325X GPU Droplets are Now Available on DigitalOcean What Makes AMD Instinct™ MI325X GPUs a Game Changer? The DigitalOcean Advantage with MI325X GPU Droplets Getting Started About the author Try DigitalOcean for free Related Articles Announcing OpenAI gpt-oss Models on the DigitalOcean Gradient™ AI Platform Build smarter AI agents: new tools now available for the DigitalOcean Gradient™ AI Platform Introducing GPU Droplets accelerated by NVIDIA HGX H200 By Waverly Swinton Published: July 17, 2025 2 min read At DigitalOcean, we’re constantly striving to equip developers and digital native enterprises with the most powerful and accessible tools to fuel AI innovation. Following the introduction of our AMD Instinct™ MI300X GPU Droplets last month, we’re thrilled to announce the availability of the next generation of AI accelerators - AMD Instinct™ MI325X. Built on the advanced AMD CDNA™ 3 architecture, the AMD Instinct™ MI325X accelerators are engineered to deliver exceptional performance for AI workloads, including large model training, fine-tuning, inference, and high-performance computing (HPC). These GPUs are available as GradientAI GPU Droplets , powerful virtual machines with a simplified setup. Key benefits of the AMD Instinct™ MI325X that enable you to accelerate complex computations, ensure your inference tasks run faster, and provide more flexibility and control: Memory capacity and bandwidth : With 256GB of HBM3E memory and 6. 0TB/s of bandwidth, the MI325X can hold massive models entirely in memory, significantly reducing the need for model splitting across GPUs and accelerating complex computations. This represents a substantial leap in memory capabilities, offering 1. 8x more capacity and 1. 3x more bandwidth compared to previous generations. Memory capacity and bandwidth : With 256GB of HBM3E memory and 6. 0TB/s of bandwidth, the MI325X can hold massive models entirely in memory, significantly reducing the need for model splitting across GPUs and accelerating complex computations. This represents a substantial leap in memory capabilities, offering 1.