defilantech

defilantech/LLMKube

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.

6 good first / help-wanted issues · Go · last activity Oct 12, 2026

ai apple-silicon autoscaling edge-computing gguf gpu homelab inference kubernetes kubernetes-operator llama-cpp llm local-llm metal mlx multi-gpu nvidia self-hosted tgi vllm
6 Open Issues Need Help Last updated: Oct 12, 2026

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priority/high component/controller component/api size/small kind/feature area/runtime good first issue
defilantech/LLMKube
232

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.

Go
#ai#apple-silicon#autoscaling#edge-computing#gguf#gpu#homelab#inference#kubernetes#kubernetes-operator#llama-cpp#llm#local-llm#metal#mlx#multi-gpu#nvidia#self-hosted#tgi#vllm
documentation size/small area/routing good first issue
defilantech/LLMKube
232

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.

Go
#ai#apple-silicon#autoscaling#edge-computing#gguf#gpu#homelab#inference#kubernetes#kubernetes-operator#llama-cpp#llm#local-llm#metal#mlx#multi-gpu#nvidia#self-hosted#tgi#vllm
defilantech/LLMKube
232

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.

Go
#ai#apple-silicon#autoscaling#edge-computing#gguf#gpu#homelab#inference#kubernetes#kubernetes-operator#llama-cpp#llm#local-llm#metal#mlx#multi-gpu#nvidia#self-hosted#tgi#vllm
defilantech/LLMKube
232

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.

Go
#ai#apple-silicon#autoscaling#edge-computing#gguf#gpu#homelab#inference#kubernetes#kubernetes-operator#llama-cpp#llm#local-llm#metal#mlx#multi-gpu#nvidia#self-hosted#tgi#vllm
defilantech/LLMKube
232

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.

Go
#ai#apple-silicon#autoscaling#edge-computing#gguf#gpu#homelab#inference#kubernetes#kubernetes-operator#llama-cpp#llm#local-llm#metal#mlx#multi-gpu#nvidia#self-hosted#tgi#vllm
defilantech/LLMKube
232

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.

Go
#ai#apple-silicon#autoscaling#edge-computing#gguf#gpu#homelab#inference#kubernetes#kubernetes-operator#llama-cpp#llm#local-llm#metal#mlx#multi-gpu#nvidia#self-hosted#tgi#vllm