December 2025 — ModelCloud/GPTQModel: Delivered cross-format GPTQ v2 and Bitblas backend support with expanded test coverage, and fixed Bitblas compatibility to support gptq_v2. This reduces upgrade risk for customers migrating to GPTQ v2 and strengthens reliability across the backend. Key accomplishments include ensuring the Bitblas backend operates with both gptq and gptq_v2 formats, and adding a forward-pass test for end-to-end validation. Technologies demonstrated include Python backend integration, conditional feature handling for format compatibility, test-driven development, and robust commit traceability.

In 2025-10, delivered GPTQv2 quantization support in the gptq_gemm kernel for tenstorrent/vllm. The change enables loading and processing models quantized with GPTQv2 by differentiating it from GPTQv1 and ensuring correct handling of zero points for low-bit or asymmetric quantization. This work expands compatibility with newer quantization specs, reducing integration risk for customers adopting GPTQv2 and enabling use of newer models in production. Impact includes broader model support, smoother deployment, and a foundation for future performance optimizations in quantized inference. Technologies/skills demonstrated include kernel-level C++ changes, quantization format handling, zero-point arithmetic, and disciplined code review and traceability.