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FreeToken open sourced: Running DeepSeek-V4-Flash on a single RTX 5090 delivers a maximum speed of 25 tokens per second.

2026.09.01 15:47:09

Beating AI News: Researchers from institutions including UC Berkeley and MIT have open-sourced FreeToken, a Mixture of Experts (MoE) inference engine built specifically for local devices, designed to solve the problem of oversized models failing to fit in video memory (VRAM). FreeToken stores most of a model’s expert weights in system memory, caching only a subset in VRAM. When an expert not present in VRAM is required, it dynamically decides whether to transfer the weights to the GPU or offload computation directly to the CPU based on PCIe and CPU memory bandwidth, enabling collaborative inference across CPU, GPU, system memory and PCIe. According to paper benchmarks, a gaming desktop equipped with an RTX 5090 runs the 284B-parameter DeepSeek-V4-Flash at 22–25 tokens per second. The model only activates approximately 13B parameters per generated token, making it particularly well-suited for this CPU-GPU hybrid inference approach. Additional test results show FreeToken runs a 35B model at 39.3 tokens per second on an RTX 4060 laptop with 8GB VRAM; a single 96GB RTX PRO 6000 can run the 753B-parameter GLM-5.2 at roughly twice the speed of llama.cpp. The project currently supports over 20 MoE models and is open-sourced under the Apache 2.0 license.

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