Ivan Fioravanti Joins X
deepseek inference
| Source: Mastodon | Original article
Ivan Fioravanti enables custom quantization on DeepSeek V4 Flash via MLX. This aids local execution and memory optimization for large models.
Ivan Fioravanti has announced that custom quantization recipes can be applied to DeepSeek V4 Flash on MLX, a significant development for developers interested in running large models locally and optimizing memory. This update is particularly noteworthy for those working with deep learning and inference, as it suggests potential improvements in model efficiency.
As we reported on May 15, Ivan Fioravanti has been actively exploring the capabilities of MLX and DeepSeek, and this latest update builds on that work. The lack of specific performance metrics or implementation details means that the full implications of this development are still unclear, but it is a promising sign for developers seeking to optimize model performance.
Looking ahead, it will be important to watch for further updates from Ivan Fioravanti and other developers working with MLX and DeepSeek, as well as any potential applications of custom quantization recipes in other areas of AI research. With the growing interest in running large models locally and optimizing memory, this development has the potential to be an important step forward for the field.
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