When it comes to deploying local LLMs, many people may think that spending more money will deliver more performance, but it's far from reality.  That's ...

Local LLMs are incredibly powerful tools, but it can be hard to put smaller models to good use in certain contexts. With fewer parameters, they often know less, though you can improve their ...

Deploying a custom language model (LLM) can be a complex task that requires careful planning and execution. For those looking to serve a broad user base, the infrastructure you choose is critical.

For the last few years, the term “AI PC” has basically meant little more than “a lightweight portable laptop with a neural processing unit (NPU).” Today, two years after the glitzy launch of NPUs with ...

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Dell has just unleashed its new PowerEdge XE9712 with NVIDIA GB200 NVL72 AI servers, with 30x faster real-time LLM performance over the H100 AI GPU. Dell Technologies' new AI Factory with NVIDIA sees ...

Model selection, infrastructure sizing, vertical fine-tuning and MCP server integration. All explained without the fluff. Why Run AI on Your Own Infrastructure? Let’s be honest: over the past two ...

Xiaomi is reportedly in the process of constructing a massive GPU cluster to significantly invest in artificial intelligence (AI) large language models (LLMs). According to a source cited by Jiemian ...

A new vulnerability dubbed 'LeftoverLocals' affecting graphics processing units from AMD, Apple, Qualcomm, and Imagination Technologies allows retrieving data from the local memory space. Tracked as ...

Developing AI and machine learning applications requires plenty of GPUs. Should you run them on-premises or in the cloud? While graphics processing units (GPUs) once resided exclusively in the domains ...