Someone actually tested Mistral Medium 3.5 on AMD's new Strix Halo chip, and the results are... not great. For a 48k-token prompt with 4k thinking tokens, it took about 2 hours just to get an answer about code architecture. Yeah, you read that right—two hours. The takeaway: if you want to run this locally on Strix Halo, queue it up before bed. The technical setup involved heavy optimization (Q5_K_XL quantization, GPU acceleration with -ngl 999, cache reuse), but even with all that tuning, it's still a crawl. Not exactly the "instant local AI" dream, but hey, at least it works.
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Tech Blogger Take
Someone waited 2 hours for AI to answer a coding question. This is why local AI isn't ready.
Picture this: you ask your local AI a complex coding question, hit enter, and then... wait. For two hours. That's exactly what happened when someone tested Mistral Medium 3.5 on AMD's shiny new Strix Halo chip. We're talking about a 48k-token prompt with 4k thinking tokens — basically asking the AI to really think through a code architecture problem. Even with every optimization trick in the book (Q5_K_XL quantization, maxed-out GPU acceleration, cache reuse), it still crawled along like dial-up internet. This isn't some budget setup either — Strix Halo is AMD's flagship AI chip. The brutal reality? While everyone's hyping 'AI on your laptop,' we're still light-years away from the instant, ChatGPT-like experience people expect. Sure, it works, but 'working' and 'practical' are two very different things.
AI Analysis
Hardware Development
highStart planning AI workload benchmarks for your next chip designs — consumer expectations are shifting from 'can it run' to 'how fast can it run'
Even AMD's flagship Strix Halo with all the optimization tricks still needs 2 hours for what cloud APIs do in seconds — the local AI performance gap is massive
Your customers are about to discover that 'AI-capable hardware' and 'AI-practical hardware' are completely different things
Job Impact Analysis
AI Engineers
Role ShiftLocal AI inference is proving painfully slow even on premium hardware, forcing a complete rethink of deployment strategies
Start building hybrid workflows now — queue long inference jobs locally overnight, keep interactive work in the cloud
Product Managers
At RiskThe 2-hour wait time for complex AI tasks on local hardware kills any real-time product experience dreams
Redesign your AI features around batch processing and overnight workflows, not instant responses