Deploy the SUSE AI stack on a Vultr Bare Metal or Cloud GPU server using Terraform and Ansible, with Rancher Prime, SUSE Storage, Observability, and Ollama.
Deploy NVIDIA Dynamo disaggregated serving with AIConfigurator and RDMA to optimize large-model LLM inference throughput across multi-GPU environments.
Enable observability in NVIDIA Dynamo inference pipelines using Prometheus metrics, OpenTelemetry tracing, and Grafana dashboards for distributed GPU workloads.
Deploy NVIDIA Dynamo KVBM to enable KV cache offloading across GPU, CPU, and disk tiers for efficient distributed LLM inference.
Deploy NVIDIA Dynamo KV-aware routing for distributed LLM inference to reduce TTFT, improve throughput, and optimize GPU utilization.
Deploy NVIDIA Dynamo SLA Planner on Kubernetes for automated, SLA-driven GPU autoscaling and intelligent LLM resource optimization with Prometheus and Grafana integration.
Deploy NVIDIA Dynamo with TensorRT-LLM for high-performance, distributed GPU inference using aggregated and disaggregated serving architectures.
Deploy NVIDIA Dynamo with SGLang for scalable, high-performance GPU-powered LLM inference using aggregated and disaggregated serving architectures.
Deploy NVIDIA Dynamo with vLLM for high-throughput, low-latency LLM inference using aggregated and disaggregated GPU serving architectures.
Install and run Code Llama on a Vultr Cloud GPU for AI-powered coding tasks.
Learn how to set up and configure N8N on Vultr Cloud GPU to create powerful AI workflows. Step-by-step guide for automating AI tasks efficiently.
Learn how to deploy a Linux VDI solution on Vultr Cloud GPU using Cendio ThinLinc. Step-by-step guide for setting up remote desktop infrastructure efficiently.