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11 articles
This article explains how the surge in demand for Apple's Mac mini on eBay reflects the growing need for local AI inference hardware, demonstrating the intersection of AI model complexity, hardware scarcity, and market dynamics in the emerging AI ecosystem.
Learn how Google and NVIDIA are making AI inference cheaper and faster through new hardware and software integration. This breakthrough could make AI more accessible to businesses and improve everyday applications.
Learn how advanced AI optimization techniques enable repurposing old tablets as smart home control panels through edge computing and model compression.
This article explains vision-language models and how Liquid AI's new LFM2.5-VL-450M model brings powerful AI capabilities to edge devices.
NVIDIA's KVPress offers a memory-efficient solution for long-context language model inference through advanced KV cache compression, enabling more scalable AI applications.
This explainer explores how the MacBook Air M5 chip's advanced neural engine architecture enables powerful on-device AI processing, fundamentally changing how we think about edge computing and mobile artificial intelligence.
Google introduces TurboQuant, a new compression algorithm that reduces LLM key-value cache memory by 6x and delivers up to 8x speedup without accuracy loss.
Israeli AI startup NeuReality has appointed former Google AI director Shalini Agarwal to guide its NR-NEXUS inference operating system into the market.
Paged Attention emerges as a key solution to the GPU memory bottleneck in large language models, enabling more efficient memory usage and higher concurrency in AI inference systems.
Learn what AI inference chips are, how they work, and why they're crucial for making AI systems faster and more efficient. This explainer explains the basics of inference chips using simple analogies.
Gimlet Labs raises $80 million Series A to solve AI inference bottlenecks across multiple chip architectures including NVIDIA, AMD, and Intel.
