October 2025 — Delivered end-to-end AWQ quantization workflow for Qwen3-VL-30B-A3B-Instruct in vllm-project/llm-compressor. Implemented an example script that initializes model and processor, prepares a calibration dataset, configures AWQ parameters, performs one-shot quantization, demonstrates sample generation, and saves the quantized model and processor. Commit reference included: 37cfe8ec141e5246b5decbf4d8f9d411c492866c.

Month: 2025-08 — Key deliverables focused on ROCm stability, CPU offloading, and MoE quantization for ROCm, spanning two repositories (intel/auto-round and tenstorrent/vllm). The work enhances performance on low-memory GPU setups, broadens hardware compatibility, and improves runtime resilience for MoE-based inference. Key features delivered: - ROCm Out-of-Memory Error Handling Enhancement for CPU Offloading in Low-Memory GPUs (intel/auto-round). Adds ROCm-specific OOM handling to stabilize CPU offloading on constrained GPU configurations. - MoE GPTQ quantization enhancements for ROCm with fallback and config fix (tenstorrent/vllm). Introduces GPTQ quantization support for MoE on ROCm with a fallback path and config robustness for Qwen3-MoE. Major bugs fixed: - ROCm GPU backend compatibility for AITER support (tenstorrent/vllm). Disables rocm_aiter_fa backend for ROCm GPUs not supporting AITER to improve stability across diverse hardware. - KeyError in Qwen3-MoE GPTQ quantization on ROCm (tenstorrent/vllm). Fixes KeyError 'layers.14.mlp.gate.g_idx' and improves config reliability. Overall impact and accomplishments: - Improved stability and performance of CPU offloading on low-memory ROCm systems, reducing OOM-related stalls and crashes. - Broadened ROCm hardware support for MoE quantization workflows, enabling more deployments and smoother inference for large models. - Reduced runtime errors and misconfigurations through targeted fixes and safer back-end disabling on unsupported GPUs. Technologies/skills demonstrated: - ROCm-aware optimization, GPU memory management, and CPU offloading strategies - GPTQ quantization for MoE, Qwen3-MoE compatibility, and MoE config fixes - Backend compatibility strategies (AITER) and robust feature gating - Code review and commit discipline across two repos (commit references included)