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NVIDIA's GH200 NVL32 system shows significant improvements in time-to-first-token performance for large language models, enhancing real-time AI applications. (Read More)
Ollama makes it easier to run Meta's Llama 3.2 model locally on AMD GPUs, offering support for both Linux and Windows systems. (Read More)
TEAL offers a training-free approach to activation sparsity, significantly enhancing the efficiency of large language models (LLMs) with minimal degradation. (Read More)
LangGraph v0.2 introduces new checkpointer libraries, including SQLite and Postgres options, to enhance customization and resilience in LLM applications. (Read More)
Circle and Blockchain at Berkeley introduce TXT2TXN, an AI-driven tool using Large Language Models to simplify blockchain transactions through intent-based applications. (Read More)
NVIDIA's TensorRT-LLM and Triton Inference Server optimize performance for Hebrew large language models, overcoming unique linguistic challenges. (Read More)
LangSmith introduces dynamic few-shot example selectors, allowing for improved LLM app performance by dynamically selecting relevant examples based on user input. (Read More)
AMD's Radeon PRO GPUs and ROCm software enable small enterprises to leverage advanced AI tools, including Meta's Llama models, for various business applications. (Read More)
Character.AI announces a strategic agreement with Google and key leadership changes to accelerate the development of personalized AI products. (Read More)
NVIDIA's Blackwell architecture sets new benchmarks in MLPerf Inference v4.1, showcasing significant performance improvements in LLM inference. (Read More)
After previously opposing another AI-related bill, SB 1047, OpenAI has expressed support for AB 3211, which would require watermarks on AI-generated content.
MIT's new study reveals how large language models (LLMs) can efficiently detect anomalies in critical infrastructure systems, offering a plug-and-play solution. (Read More)
AI21 Labs introduces Jamba 1.5, a new family of large language models leveraging hybrid architecture for superior reasoning and long context handling. (Read More)
Explore how Large Language Models (LLMs) choose the next word using decoding strategies. Learn about different methods like greedy search, beam search, and more. (Read More)
Anyscale's latest blog post delves into Direct Preference Optimization (DPO) with synthetic data, highlighting its methodology and applications in tuning language models. (Read More)
NVIDIA experts share strategies to optimize large language model (LLM) inference performance, focusing on hardware sizing, resource optimization, and deployment methods. (Read More)
NVIDIA NIM microservices optimize throughput and latency for large language models, improving efficiency and user experience for AI applications. (Read More)
NVIDIA introduces structured pruning and distillation methods to create efficient language models, significantly reducing resource demands while maintaining performance. (Read More)
NVIDIA's NeMo Curator offers a streamlined method for fine-tuning large language models (LLMs) with custom datasets, enhancing machine learning workflows. (Read More)
NVIDIA NIM introduces support for multilingual large language models, improving global business communication and efficiency with LoRA-tuned adapters. (Read More)
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