DeepSeek's new model sets a template for powerful LLMs that run lean
DeepSeek has released V4.1 Flash, a large language model with 763 billion parameters that significantly reduces memory requirements through architectural innovations. The update introduces a conditional memory module using N-gram parameters to decouple memory from computation, allowing for more efficient resource usage. These changes enable the model to support four to eight times as many users within the same KV cache footprint as its predecessor.
- ▪DeepSeek V4.1 Flash contains 763 billion parameters, making it more than 2.5 times larger than the model it replaces.
- ▪The model utilizes a new causal encoder-decoder and updated attention mechanisms to cut KV cache consumption to 13-25% of previous requirements.
- ▪196 billion of the model's parameters are N-gram parameters that form a conditional memory module to improve efficiency.
- ▪The architectural changes allow the model to serve four to eight times as many users in the same memory footprint as DeepSeek V4 Flash.
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| Publication time | Fri, 11 Sep 2026 09:15:00 +0200 |
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(function() { let windowUrl = window.location.href; windowUrl = windowUrl.substring(windowUrl.indexOf('?') + 1); let messageElement = document.querySelector('.shareableMessage'); if (windowUrl && windowUrl.includes('code') && windowUrl.includes('expires')) { messageElement.style.display = 'block'; } })(); AI and ML DeepSeek's new model sets a template for powerful LLMs that run lean DeepSeek V4.1 Flash proves that just because you build a bigger model doesn't mean you need more GPUs to serve it Tobias Mann Tobias Mann SYSTEMS EDITOR Published fri 11 Sep 2026 // 08:15 UTC READ MORE Hundreds of AI agents helped PaperCut attacker hit 395+ orgs, and some went off script 12 hours ago Nscale swallows lion's share of UK datacenter investment 17 hours ago d-Matrix drinks the Nvidia Kool-Aid with…
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