So you're thinking about running Qwen 3.6 35B on your laptop with an RTX 3060 6GB and 40GB RAM? Here's the real talk: it's technically possible, but you're going to hit some hard limits. A 35B model quantized to Q4 (4-bit) takes up roughly 18-20GB of VRAM, which means you'll need to offload a significant chunk to system RAM. Your RTX 3060 Mobile will handle the heavy lifting, but expect slower inference speeds—we're talking maybe 5-15 tokens/second depending on how much you're offloading. For non-time-sensitive tasks like PDF summarization, that's actually workable. The real bottleneck? Your 4-core CPU and the PCIe bandwidth between GPU and RAM will create a noticeable slowdown when swapping data. You might want to test with a smaller model first (like 13B) to see if it meets your needs without the headache. Check out frameworks like llama.cpp or Ollama to optimize for your setup.
OpenAI is hosting a livestream event. Details about the specific announcements, product launches, or demonstrations will be revealed during the broadcast.
The last time OpenAI did an unannounced livestream, they dropped GPT-4 Turbo and changed pricing overnight
OpenAI is launching ChatGPT Images 2.0 with major upgrades to image generation capabilities. Watch the livestream announcement at https://openai.com/live/
OpenAI is positioning this as a direct competitor to established image generation tools, suggesting they're confident enough to challenge the current market leaders
Throughout 2025 the standard response to any complaint about an LLM was some version of "just wait 3-6 months, the next generation will handle this effortlessly." The argument was everywhere. Every limitation was temporary, every missing capability was a few iterations away, every autonomous agent demo was a preview of imminent reality. It's April 2026 now and worth checking how that held up. On r/ClaudeAI this week there's a long thread about Opus 4.7 where multiple users argue it's a regress
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.