#autoscaling

Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.

Kubernetes Operator for Cloud Spanner autoscaling

KEDA External gRPC Scaler for GPU workloads - native NVML metrics via DaemonSet, no Prometheus required

Tensor Fusion is a state-of-the-art GPU virtualization and pooling solution designed to optimize GPU cluster utilization to its fullest potential.

Kubernetes PVC autoscaler

Terraform module to create AWS Auto Scaling resources 🇺🇦

Multi-tenant Kubernetes operator for self-hosted GitHub Actions runners. Scale-to-zero workers, per-tenant egress IP pools, and GPU priority scheduling across a shared ResourceQuota — an Actions Runner Controller (ARC) alternative.