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RaiderChip NPU for LLM at the Edge supports DeepSeek-R1 reasoning modelsThe rise of optimized reasoning models, capable of matching the performance of massive solutions like ChatGPT, strengthens RaiderChip’s commitment to AI acceleration through its affordable and high-performance edge devices. Spain, February 17th, 2025 -- RaiderChip, a leading fabless semiconductor company specializing in hardware acceleration for Generative Artificial Intelligence, has added the DeepSeek-R1 family of reasoning LLMs to the growing list of models supported on its GenAI NPU accelerator, with the fundamental capability to allow users to swap LLM models on the fly, thanks to the flexibility of its hardware design. This integration marks a significant breakthrough in local Generative AI inference by combining RaiderChip’s optimized architecture for affordable devices with the outstanding computational efficiency of DeepSeek-R1. The new DeepSeek-R1 LLM family, developed in China, has recently revolutionized the industry, and stands out for its exceptional balance between operational cost and cognitive performance. Despite its compact design, it outperforms larger models in efficiency and capability, challenging the traditional strategy of massive proprietary LLMs relying on cloud-based infrastructure. The future of Artificial Intelligence is moving toward more compact, optimized, and specialized models that can run at the Edge, reducing the high costs of inference. Víctor López, CTO of RaiderChip, highlights: “By combining our stand-alone hardware NPU semiconductors with all of DeepSeek-R1’s distilled models, we provide our customers with exceptional performance without relying on costly cloud infrastructure. Additionally, we offer greater independence, security and privacy for their solutions, guaranteeing AI-service availability and low-latency, supporting the customization of extraordinarily intelligent models, ultimately enabling the highest-performing AI Agents at the Edge”. WANT TO KNOW MORE? SEE WHAT’S INSIDE OUR AI ACCELERATORS
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