We have been experimenting with whether strong multi-step reasoning actually requires a full-precision scale. Alpie Core is a 32B parameter model trained and s

We have been experimenting with whether strong multi-step reasoning actually requires a full-precision scale. Alpie Core is a 32B parameter model trained and served entirely at 4-bit precision. Instead of training in FP16 and compressing later, we optimized the model end-to-end for low-precision reasoning. Despite the constraint, it performs competitively on reasoning and coding benchmarks like GSM8K, BIG-Bench Hard, and SWE-Bench Verified, while using far less memory and infrastructure. We are especially interested in feedback on long-context behaviour, reasoning stability across retries, and how it performs inside agent or tool-using workflows.