Lightweight TEE-based system for secure ONNX model inference using Intel SGX with automated model partitioning to fit within enclave memory constraints.
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Updated
Oct 12, 2026 - C
Lightweight TEE-based system for secure ONNX model inference using Intel SGX with automated model partitioning to fit within enclave memory constraints.
Multi-SLO, partition-aware batching and provisioning for cost-efficient DNN inference on serverless platforms (built on HarmonyBatch and Gillis)
Hunyuan3D-2 fork — image→textured 3D→sliced STL + part segmentation. RTX 50-series (Blackwell/sm_120), CUDA 13.0, Python 3.12, PyTorch 2.11+cu130.
Distributed layer inference for transformer LLMs on edge K3s clusters, with Python/PyTorch and native C++/llama.cpp runtimes, GGUF stage shards, Kubernetes manifests, and an Ops UI for monitoring experiments.
Proof-of-concept secure model-distributed LLM inference: layer partitioning across nodes, recurrent pipeline parallelism, AES-GCM encrypted comms, commitment-based verification & HMAC auth.
Transfer-aware carbon-efficient scheduling framework for simulating distributed deep neural network inference at the edge.
Web-Based Distributed LLM Inference
Experimental testbed for partitioned inference of deep neural networks and large language models across Raspberry Pi edge devices and an edge server, orchestrated by the NNPM web application.
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