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FreeToken Dynamically Allocates CPU, GPU, and PCIe Resources to Run a 35B MoE Model with 8GB of VRAM

Instead of using fixed offloading, FreeToken uses measured bandwidth to decide whether to transfer cache-missed experts to the GPU or execute them directly on the CPU. The paper also demonstrates a 753B model served by a single workstation GPU, but the full weights still require system memory; this should not be interpreted as “fitting all 753B parameters on one GPU.”

最新模型

Marin Launches Open Training of a 535B-A23B Model, Publicly Trackable Across 11 GB200 NVL72 Systems

Marin has begun training a mixture-of-experts (MoE) model with 535B total parameters and approximately 23B active parameters per token. The project plans to use 11 GB200 NVL72 systems to process roughly 18T to 18.75T tokens. Configurations, scaling experiments, and real-time telemetry are public, but model quality, the long-context strategy, and the final token budget have yet to be finalized.

最新模型

IAR Splits Document Internalization Into Injection, Question-Answer Alignment, and Weight Merging, Preserving More General Capabilities on Average

A Beijing Academy of Artificial Intelligence research team has proposed a three-stage post-training method that enables models to answer questions about a fixed document set without RAG. IAR outperformed standard SFT across all metrics in seven of eight experiments, although the results still depend on synthetic question-answer pairs, model judges, and model-specific hyperparameter tuning.