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Ryzen AI Halo Is Not New Hardware – It’s AMD’s Strix Halo AI Developer Platform

AMD Ryzen AI Halo is being marketed as a new local AI development solution, but it is important to be precise about what it actually is. Ryzen AI Halo does not introduce new silicon, new performance characteristics, or a faster variant of Strix Halo. It is a reference mini PC platform built around the already available Ryzen AI Max+ 395, bundled with a curated and validated s...


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AMD Strix Halo ROCm Crashes: Firmware Fix Is the Key Update

If you own a Strix Halo system and tried to run ROCm workloads for local LLM inference, you probably ran into hard crashes, GPU hangs, or instant failures when loading models. Most users discovered quickly that Vulkan-based paths kept working, while ROCm was effectively unusable. That behavior was the clue to what was really broken. This issue is now largely resolved, but the...


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The Local LLM Desktop Hack: A Full RTX 5090 System for Less Than the GPU

If you are on the market for a desktop computer for local LLM inference, these are some of the best prebuilt deals available in Q1 2026. This guide is specifically for users who either do not want to build a system themselves or do not have the time to source parts in the current market. Under normal conditions, building your own system is almost always cheaper. Right now, th...


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GPU Prices Are Rising Again: Is Now the Right Time to Buy an LLM-Capable GPU?

After a brief summer window where prices cooled and availability improved, the GPU market is tightening again. For local LLM enthusiasts, this shift is not subtle. High-VRAM cards are becoming harder to find, and prices across both new and second-hand markets are moving up. The underlying reasons are structural, not seasonal, and they point to more pressure ahead. The core is...


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Google Says the Quiet Part Out Loud: LLM Inference Is Starved by Memory

Google recently published a hardware-focused paper that says the quiet part out loud: modern LLM inference is bottlenecked by memory bandwidth and memory latency, not compute. This is not news to anyone running models locally, but the paper matters because it confirms this at the datacenter scale and explains why GPUs keep getting faster while real-world token generation bare...


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