LLM inference
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Accelerating LLM and VLM Inference with TensorRT Edge-LLM
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NVIDIA TensorRT Edge-LLM is a new open-source C++ framework designed to accelerate large language model (LLM) and vision language model (VLM) inference for real-time applications in automotive and robotics. It addresses the need for low-latency, reliable, and offline operations directly on embedded platforms like NVIDIA DRIVE AGX Thor and NVIDIA Jetson Thor. The framework is optimized for minimal resource use and includes advanced features such as EAGLE-3 speculative decoding and NVFP4 quantization support, making it suitable for demanding edge use cases. Companies like Bosch, ThunderSoft, and MediaTek are already integrating TensorRT Edge-LLM into their AI solutions, showcasing its potential in enhancing on-device AI capabilities. This matters because it enables more efficient and capable AI systems in vehicles and robots, paving the way for smarter, real-time interactions without relying on cloud-based processing.
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Visualizing the Semantic Gap in LLM Inference
Read Full Article: Visualizing the Semantic Gap in LLM InferenceThe concept of "Invisible AI" refers to the often unseen influence AI systems have on decision-making processes. By visualizing the semantic gap in Large Language Model (LLM) inference, the framework aims to make these AI-mediated decisions more transparent and understandable to users. This approach seeks to prevent users from blindly relying on AI outputs by highlighting the discrepancies between AI interpretations and human expectations. Understanding and bridging this semantic gap is crucial for fostering trust and accountability in AI technologies.
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Automating ML Explainer Videos with AI
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A software engineer successfully automated the creation of machine learning explainer videos, focusing on LLM inference optimizations, using Claude Code and Opus 4.5. Despite having no prior video creation experience, the engineer developed a system that automatically generates video content, including the script, narration, audio effects, and background music, in just three days. The engineer did the voiceover manually due to the text-to-speech output being too robotic, but the rest of the process was automated. This achievement demonstrates the potential of AI to significantly accelerate and simplify complex content creation tasks.