Xiaomi Announces Its Fastest AI Model yet with 1000 Token/second Speed
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6 Articles
Xiaomi MiMo AI Model Hits 1,000 Tokens Per Second With Huge Breakthroughs
Xiaomi MiMo: Cerebras needed a wafer-scale chip the size of a dinner plate. Groq built custom silicon with on-chip SRAM from the ground up. Xiaomi used a standard eight-GPU server node — the kind any developer can rent from a cloud provider today. Xiaomi, in collaboration with inference partner TileRT, has officially launched MiMo-V2.5-Pro-UltraSpeed, achieving […]
What we must remember: Xiaomi displays more than 1,000 tokens per second on a 1000 billion-parameter d的IA model. The exploit is based on standard GPUs, without a custom chip, thanks to extensive software work. Three innovations combine: the quantization FP4, the speculative decoding DFlash and the TileRT engine. Xiaomi has just pushed the race to speed in artificial intelligence. In collaboration with the TileRT team, the group unveiled MiMo-V2.…
Xiaomi announces its fastest AI model yet with 1000 token/second speed
Xiaomi‘s large language model family, MiMo, has officially launched UltraSpeed mode for MiMo-V2.5-Pro. Developed jointly with TileRT, the 1-trillion-parameter model can run on general-purpose GPUs while breaking the 1,000 tokens-per-second generation barrier. Xiaomi says this milestone is possible through the “ultimate co-design” of the model and its underlying system. Make a Snake game in 10 seconds To put that in perspective, MiMo-V2-Flash, an…
Xiaomi MiMo and TileRT Push a 1-Trillion-Parameter Model Past 1000 Tokens Per Second on Commodity GPUs
Inference speed is becoming a competitive metric for large language models. Xiaomi’s MiMo team just released MiMo-V2.5-Pro-UltraSpeed, built in collaboration with the TileRT systems group. It decodes faster than 1000 tokens per second on a 1-trillion-parameter model. Xiaomi team describes this as a first at trillion-parameter scale. Demos show generation peaks near 1200 tokens per second. The notable part is the hardware: it runs on commodity GP…
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