memory limits
memory limits on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on memory limits in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around memory limits, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
AI’s memory crunch is coming for Android apps
The escalating demands of AI are creating a tangible memory crunch, and Android apps are next in line. Google is implementing stricter memory-use limits across Android to address hardware shortages fueled by burgeoning AI data centers—a shift that will likely impact lower-cost smartphones. This move signals a necessary evolution in mobile resource management. For a glimpse into the broader implications of AI-driven hardware innovation, explore our article on Hugging Face’s Microduck robot.
![Trained an diffusion model that runs on 264KB of RAM [P]](https://preview.redd.it/8vzcg2x7q3kh1.png?width=140&height=140&auto=webp&s=7a1fb828642c6ddfa5211c2a6f942764e68a21cb)
Trained an diffusion model that runs on 264KB of RAM [P]
Pushing the boundaries of on-device AI, a recent project demonstrated image generation using a diffusion model trained on a microcontroller with a mere 264KB of SRAM. Despite limitations—including heavy quantization and memory constraints—the resulting 32x32 pixel images yielded surprisingly compelling results. The experiment highlighted a critical performance bottleneck: parallel processing, while intended to accelerate calculations, ultimately slowed down the system due to excessive I/O. This fascinating exploration underscores the challenges and potential of resource-constrained AI, as explored further in "Ten Is Not a Hundred."
Tried testing qwen 35b moe model on s26 ultra , without compromising on precision [R] ,[D]
Early testing reveals promising results for running a private Qwen 35B MoE LLM on an S26 Ultra, demonstrating a potential for approximately 90 tokens/second input processing and 8 tokens/second output generation after optimization. This achievement, realized through self-directed AI/ML exploration and leveraging available compute resources, highlights the accessibility of advanced model deployment. The author, without disclosing implementation details, is actively seeking collaborators to further test and refine this mobile runtime.