With all resources managed by a single orchestration layer, our clients take full advantage of increased portability, less overhead, and less management complexity compared to traditional VM-based deployments.
Thanks to container image caching and specialized schedulers, your workload can be up and running in as little as 5 seconds.
Access a massive amount of resources in the same cluster, instantly. Simply request the CPU cores and RAM you need, with an optional amount of GPUs, and you’re off to the races.
50GRAMx handles all of the control-plane infrastructure, cluster operations and platform integrations so you spend more time building products. With all resources available via Kubernetes, you get unmatched flexibility and performance with less infrastructure overhead.
GPUs are advancing AI at unimaginable scale, changing how films and episodic television are created, accelerating breakthroughs in synthetic biology, and powering the Metaverse.
Deploy inference with a single YAML. We support all popular ML Frameworks: TensorFlow, PyTorch, SKLearn, TensorRt, ONNX as well as custom serving implementations. Optimized for NLP with streaming responses and context aware load-balancing.
We build our distributed training clusters with a rail-optimized design using NVIDIA Quantum InfiniBand networking and in-network collections using NVIDIA SHARP to deliver the highest distributed training performance possible.
Leverage container auto-scaling in render managers - like Deadline - to go from a stand-still to rendering a full VFX pipeline in seconds.
Leverage powerful Kubernetes native workflow orchestration tools like Argo Workflows to run and manage the lifecycle of parallel processing pipelines for VFX rendering, health sciences simulations, financial analytics and more.
Built on top of Kubernetes, get NVIDIA GPU-accelerated and CPU-only Virtual Servers that are highly configurable, affordable and available at scale.
Learn More →Access the industry’s broadest selection of high-end NVIDIA GPUs on 50GRAMx Cloud, purpose-built for large-scale GPU-accelerated workloads and served on-demand.
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