…a lightweight, C/C++ agentic framework designed for seamless, native in-game integration. It is an open source, fully customizable solution optimized for small models and hardware-accelerated on NVIDIA RTX. Developers…
…Building the TensorRT-LLM engine Before running TensorRT-LLM, you build the inference engine by compiling a set of binaries that take into account optimizations for the specific GPU hardware, model architecture…
…Day 0 fine-tuning with NeMo Framework Developers can customize Gemma 4 with their own domain data using the NVIDIA NeMo framework , specifically the NeMo Automodel library, which combines native PyTorch ease…
…Add CloudXRKit as a dependency in your Xcode project, initialize a streaming session pointed at your server, and the framework handles hardware-accelerated video decoding, spatial tracking via ARKit, and local scene…
…How inference frameworks coordinate scaling Inference frameworks address scaling at the application level with custom autoscalers that have visibility into inference-specific metrics. llm-d’s workload variant autoscaler (WVA) monitors per…
…Hardware advancements in Blackwell Ultra GB300, including larger memory and power budgets, enabled higher model locality and throughput compared to GB200, while the full-stack optimization across NVIDIA libraries and frameworks (cuDNN…
…At its foundation, the platform maintains full CUDA backward compatibility across hardware generations, ensuring existing models, frameworks, and workflows run seamlessly while automatically benefiting from generational improvements in compute, memory, and interconnect…
…Popular inference frameworks, including NVIDIA TensorRT-LLM , vLLM, and SGLang, also support running models in NVFP4 format today with models available in NVFP4 variants. For example, on HuggingFace, developers can find ready…
…The model runs efficiently across a range of NVIDIA hardwareincluding NVIDIA H100, DGX Spark, DGX Station, and RTX/RTX PROdelivering lower serving costs, high concurrency, and improved responsiveness for enterprise AI applications…
…Framework that handles tokenization, batching, KV-cache management, and hardware-accelerated execution so models meet real-time or near–real-time latency targets on the target SoC or cloud path. Start with…