Local inference of large language models (LLMs) is taking a leap forward.

Accelerated Inference on Silicio

ChatJimmy.ai announced achieving a speed of 15,414 tokens per second, using a proprietary technology called "mask ROM recall fabric". Essentially, the model weights are etched directly into the silicio, creating an Application-Specific Integrated Circuit (ASIC) dedicated to inference.

Implications for AI Hardware

This approach eliminates the need for HBM or VRAM, removing potential bottlenecks. The discussion now revolves around whether to invest in general-purpose AI hardware, such as Gigabyte AI TOP ATOM units based on NVIDIA Spark/Grace Blackwell architecture, or wait for the widespread adoption of these specialized ASICs.

Future Considerations

The key question is whether this technology will mark the beginning of an era in which LLM inference will be dominated by dedicated chips, rendering general-purpose GPU-based approaches obsolete.