Specialized Infrastructure

For Model Training and Fine-Tuning

Training Machine Learning models, especially modern Deep Neural Networks, is at the heart of Networke Cloud's architecture. The entire Networke stack is designed for highly scalable and cost-efficient model training.

• Bare-Metal Nodes with NVIDIA GPUs: Networke’s bare-metal nodes feature a wide range of NVIDIA GPUs, delivering exceptional computational power for intensive workloads.

• InfiniBand Interconnect: Networke’s network stack incorporates InfiniBand, providing ultra-fast, low-latency connections to accelerate distributed training.

• High-Performance Storage: Network-attached storage operates at terabit speeds, allowing checkpoints to load and write seamlessly to our control plane, enabling large-scale distributed training jobs to scale in seconds.

• Expert Support: Beyond the core tech stack, Networke offers in-depth expertise and supports customers with cutting-edge ML research. Through in-house knowledge, industry partnerships, and collaboration with research organizations, Networke’s team excels in optimizing large transformer models, enhancing Hugging Face workflows, and selecting ideal hardware for any ML task.

For Inference

Networke Cloud's inference engine dynamically autoscales containers based on demand, ensuring fast and efficient fulfillment of user requests. Containers automatically scale down when demand decreases, conserving GPU resources and minimizing costs for inactive workloads.

Networke’s rapid autoscaling capabilities provide a highly responsive service compared to other cloud providers. Scaling up containers and allocating new resources on Networke can take just seconds—for instance, spinning up the 6B GPT-J model on Networke Cloud can be done in under fifteen seconds.

Additionally, Networke Cloud’s inference stack is powered by well-supported, industry-standard open-source tools:

• Knative Serving: Acts as the serverless runtime, managing autoscaling, revision control, and canary deployments seamlessly.

• KServe: Offers an intuitive interface via Kubernetes resource definitions, simplifying the deployment of models without the complexity of configuring underlying frameworks like TensorFlow.