What if you could run GPT-class models at a fraction of the compute cost? Multiverse Computing just launched a public API and demo app for its compressed versions of AI models from OpenAI, Meta, DeepSeek, and Mistral AI.
Model compression shrinks large AI models so they run faster and cheaper without losing much accuracy - think of it like converting a high-res video to a smaller file that still looks good. Multiverse's approach uses tensor network methods borrowed from quantum physics to reduce model sizes while keeping performance close to the originals. The new API lets developers integrate these smaller, faster models into their own products, while a companion app lets anyone test the compressed models side by side with the originals.
For companies spending heavily on inference costs (the per-query expense of running an AI model), smaller models that perform nearly as well could mean meaningful savings. The catch, as always with compression claims, is whether these models hold up in production. Benchmarks and real-world workloads are different things, and the AI industry has no shortage of optimization claims that fall apart on edge cases. But the value proposition is clear: if you can run a compressed version of GPT or Llama at a fraction of the compute cost with minimal quality loss, the economics of AI applications shift. Pricing details for the API were not included in the announcement.