
NVIDIA has launched the Jetson T3000 and T2000 modules, designed to enhance robotics and edge AI applications. These modules, part of the Thor architecture, offer significant AI compute power in a compact form, facilitating the deployment of intelligent machines in various industries. The T3000 delivers 865 FP4 teraflops of AI compute, while the T2000 provides a more accessible entry point for edge AI systems. This development expands NVIDIA's edge AI platform, offering scalable solutions and simplifying development with new agent skills for memory optimization.
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This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 across Linux and Windows. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, while Apple Silicon builds now ship KleidiAI disabled by default, shifting the focus back to standard ARM optimizations. The inclusion of Snapdragon support for Linux and Android marks a significant step toward on-device AI beyond the x86/ARM desktop ecosystem. It is less about new features and more about ensuring llama.cpp remains the universal runtime for every available accelerator.
Speculative decoding just got a significant speed boost on NVIDIA GPUs. This release introduces a specialized kernel for 'few-row' matrix multiplications, targeting the small batch sizes typical of verification passes in speculative decoding workflows. By optimizing these specific operations for quantized formats like Q8_0 and IQ4 variants, llama.cpp reduces latency where it matters most during token generation. The change is CUDA-only for now but leverages hardware capabilities from Ampere onwards, making local inference faster without requiring new model architectures or complex configuration changes.
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NVIDIA Unveils Jetson Thor for Robotics and Edge AI
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