DeepSeek has launched its V4 model series, featuring a 1.6-trillion parameter open-weights Mixture-of-Experts model that challenges proprietary frontier systems.
DeepSeek has released the DeepSeek-V4 model series, featuring the flagship 1.6-trillion parameter DeepSeek-V4-Pro MoE model alongside the 284-billion parameter DeepSeek-V4-Flash. Designed to rival top closed-source frontier models, DeepSeek-V4 supports a 1-million-token context window powered by efficient compressed attention mechanisms. The model series is notable for its inference efficiency—activating only 49 billion parameters per token—and its training optimization on Huawei Ascend 950PR hardware, demonstrating high-end capability independent of Nvidia infrastructure.
DeepSeek is aggressively redefining the cost-to-performance ratio of frontier LLMs, proving that massive scale and hardware independence are viable paths to open-weights dominance.
* The 1.6-trillion parameter MoE model activates only 49 billion parameters per token, making it incredibly inference-efficient despite its colossal scale.
* Training on Huawei Ascend silicon marks a geopolitical and supply-chain shift, showing that state-of-the-art models can be trained without Nvidia dependencies.
* Offering a 1-million-token context window with hybrid compressed attention mechanisms directly challenges closed-source providers on long-context operations.
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1h ago
2026-06-16
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1h ago
2026-06-16
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AiChinaNews