
Tom Barnatan contributed to advanced model optimization in the kvcache-ai/sglang and jeejeelee/vllm repositories, focusing on expanding quantization support and improving Mixture of Experts (MoE) architectures. He implemented FP4, FP8, and INT8 non-gated MoE support using PyTorch and Python, integrating Marlin and NVFP4 CUTLASS to enable efficient low-precision inference. His work included updating activation functions, refining weight handling, and adding robust test coverage to ensure reliability. By addressing bugs in server configuration and expert input propagation, Tom enhanced model stability and inference accuracy, demonstrating depth in backend development, debugging, and scalable machine learning system design.
February 2026: Delivered key enhancements in jeejeelee/vllm. Implemented Marlin model no-activation and multiplication support to broaden quantization and processing capabilities. Fixed shared expert input propagation in latent MoE, boosting inference accuracy and stability. These changes extend model applicability, improve reliability, and deliver tangible business value through more efficient quantization and robust MoE inference.
February 2026: Delivered key enhancements in jeejeelee/vllm. Implemented Marlin model no-activation and multiplication support to broaden quantization and processing capabilities. Fixed shared expert input propagation in latent MoE, boosting inference accuracy and stability. These changes extend model applicability, improve reliability, and deliver tangible business value through more efficient quantization and robust MoE inference.
Monthly summary for 2026-01: Focused on delivering non-gated MoE support for jeejeelee/vllm with FP8/INT8 tensor formats using Marlin and NVFP4 CUTLASS. Delivered end-to-end feature work, including new tests and adjustments to activation functions, weight handling, and quantization to enable non-gated MoE architecture and potential performance improvements in low-precision MoE workloads. This work lays the groundwork for scalable, cost-efficient inference on large models and strengthens the MoE code path with robust testing.
Monthly summary for 2026-01: Focused on delivering non-gated MoE support for jeejeelee/vllm with FP8/INT8 tensor formats using Marlin and NVFP4 CUTLASS. Delivered end-to-end feature work, including new tests and adjustments to activation functions, weight handling, and quantization to enable non-gated MoE architecture and potential performance improvements in low-precision MoE workloads. This work lays the groundwork for scalable, cost-efficient inference on large models and strengthens the MoE code path with robust testing.
December 2025 monthly summary for repository kvcache-ai/sglang. Focused on delivering substantial features to improve model efficiency and expand quantization capabilities, while stabilizing deployment configurations to reduce operational risk.
December 2025 monthly summary for repository kvcache-ai/sglang. Focused on delivering substantial features to improve model efficiency and expand quantization capabilities, while stabilizing deployment configurations to reduce operational risk.

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