feat: Enable P2P Transfers via NIXL #135
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Add complete P2P weight transfer system for GPU-to-GPU model transfers: ## Python Client - `vllm_loader.py`: Custom vLLM loaders (MxSourceModelLoader, MxTargetModelLoader) for transferring FP8 model weights BEFORE processing - `nixl_transfer.py`: NIXL transfer manager with contiguous region support - `vllm_extension.py`: vLLM worker extension for ZMQ-based transfers - `client.py`: CLI client for P2P operations - `types.py`: TensorDescriptor and WorkerMetadata types ## Rust Server - `p2p_service.rs`: gRPC service for PublishMetadata/GetMetadata - `state.rs`: Redis-backed P2P state manager with worker merging ## Kubernetes Examples - `vllm-source.yaml`: Source vLLM deployment with NIXL ready signal - `vllm-target.yaml`: Target vLLM deployment with coordination - `modelexpress-server.yaml`: Server with Redis sidecar - `Dockerfile.client`: Client image with vLLM loader patches ## Documentation - `CLAUDE.md`: AI assistant context for the codebase - `CONTEXT.md`: Detailed engineering context Key features: - GPU-to-GPU RDMA transfers via NIXL/UCX - FP8 model support (DeepSeek-V3, etc.) - Redis-based source-target coordination - Baseline mode with individual tensor transfers - Experimental contiguous region support (MX_CONTIGUOUS_REG)
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