…in my own local LLM workflows… Tool calling is probably the biggest gap nobody's checking. There's a benchmark called BFCLv3 that measures how reliably a model handles function calls, scored…
…Drop friction with friendlier installers, UIs, and hardware detection to make local AI mainstream. For the longest time, the conversation around local AI models revolved around quality. They were either too slow…
…Related Qwen3.5-9B tops every AI benchmark right now, but that's not how you should pick a model There's a lot more to a model than just benchmarks. How…
…felt like local AI had finally become practical on consumer GPUs . My RTX 4070 Ti's 12GB of VRAM felt like enough to stop reading benchmarks and start testing models for my…
…good rule of thumb for any local model. A couple of weeks in I realized I'd been reaching for my phone for local AI tasks a bit more than my desktop…
…Sign in to your XDA account We've reached a point where every company is releasing AI models so incredibly fast that they've started blurring together. New name, bigger benchmark numbers…
…come up whenever general-purpose local models were being discussed. I mainly use local LLMs for the same stuff I'd normally open a cloud AI for - working through research, picking apart…
…Lemonade makes it easy to determine which backend will be best for your chosen model and use case, as it ships with an inbuilt benchmark. Qwen3 Coder 30B-A3B (MoE) was a…
…coding benchmarks, which is what got people outside the usual research circles paying attention. What confused me is that R1 itself isn't really the model most people are running locally. DeepSeek…
…Now, there are a multitude of reasons as to why one would want a Gemma 4 model in their local AI stack, because to put rather simply, there are no tasks that…