Hacker reverse engineers his Samsung TV and finds on-device AI listening for your dog and baby, watching what you watch, and borrowing your soundbar’s mic if the TV doesn’t have one. Then OpenAI banned him for reverse-engineering it.
Source:
Hacker reverse engineers his Samsung TV and finds on-device AI listening for your dog and baby, watching what you watch, and borrowing your soundbar’s mic if the TV doesn’t have one. Then OpenAI banned him for reverse-engineering it.
Source:
I have a Ultra 9, 5090, and 64GB RAM. I can comfortably run a 32B model which gives me the speed and performance of the best GPT/Claude model from about two years ago. It is functional? Yes. Does it do good work? Meh. Does it compare with the latest code models out now? Not even close.
I’d recommend to try Qwen Flash Next with such hardware. You should have just enough RAM. Even Q3 should be quite good.
I’ve tried it. I find the responses of the Qwen models to be far inferior to the Gemma models.
Interesting, I had the opposite experience. Especially for tool calls. But I heard it depends on the use case: Qwen better at coding and tool calls, Gemma is better for prose.
Like all things llm, the quantisation makes the difference. Which, of course you need the vram for to have good results. Flash next works very well if you can run the higher quants.
You try that Qwen3.8:27B ternary model yet? It’s supposed to be super fast:
https://huggingface.co/penkia/TernaryQuench-Qwen3.8-27B-GGUF
With all that VRAM you’ve got, it should be able to hold a ton of context too.