April 2026: Key deliveries in NVIDIA/TensorRT-LLM focused on perf and maintenance simplification. Delivered two feature improvements: - AttentionPlugin Refactor: decoupled query/key/value (Q/K/V) tensors and added FP8 KV cache with sliding window options to boost attention performance and flexibility. Aligned implementation with the EdgeLLM interface for broader compatibility. - AutoDeploy: Removed EdgeLLM ONNX export support, including related docs and example scripts, signaling a shift away from this export path and reducing surface area for maintenance. No major bugs logged this period; work prioritized stability, performance, and strategic alignment for edge deployment workflows. Technologies and practices demonstrated include FP8 caching, sliding-window attention, interface alignment with EdgeLLM, and streamlined export pipelines.

Monthly summary for NVIDIA/TensorRT-LLM (March 2026): Key features delivered: - Model Export Enhancements: added support for embedding weights export in .safetensors format, enabled float8 quantization, and updated export pipeline to use inputs_embeds for multimodal inputs. Major bugs fixed: - None reported for this period. Overall impact and accomplishments: - Expanded export capabilities to support safer, more compact embeddings; improved deployment flexibility for multimodal models; potential reductions in memory and inference latency due to float8 quantization; contributed to a more robust and scalable export workflow. Technologies/skills demonstrated: - safetensors format integration, float8 quantization, multimodal input handling (inputs_embeds), export pipeline refactoring, version-controlled commits (e.g., 3ab7770fa17432b79fcc283556a349eec9e2f957).

February 2026 monthly summary for NVIDIA/TensorRT-LLM: Focused on test infrastructure improvements and ONNX export coverage. Key feature delivered: relocation of ONNX export test from unit test directory to integration examples directory to improve test organization and enhance integration testing coverage for ONNX export functionality. No major bugs fixed this month. The work reduces regression risk, accelerates CI feedback, and improves maintainability of ONNX export workflows.

January 2026 monthly summary: Delivered a key feature enabling EdgeLLM deployment by exporting PyTorch models to ONNX, improving interoperability and deployment efficiency for NVIDIA/TensorRT-LLM in edge environments. No major bugs reported this month. This work strengthens the model deployment pipeline, enabling faster, more reliable edge inference and expanding compatibility with DriveOS LLM. Technologies demonstrated include PyTorch model handling, ONNX export workflows, and TensorRT-LLM integration, aligned with a concrete commit reference for traceability.