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This article centers on DeepSeek's open-sourced inference framework, DeepInfer, which claims to achieve state-of-the-art throughput and cost efficiency for running large language models. By optimizing KV cache management and cluster load balancing, the system reportedly enables massive token generation rates with high profit margins, directly addressing critical infrastructure challenges in AI deployment.
[AINews] DeepSeek DeepInfer: Open Source SOTA Inference running 155T toks/day/cluster making $22m/day at ~80% margin for a 81 day payback

An open source “bomb” drops from China
Proudly brought to you by SerpAPI: Today’s release covers a LOT of ground, but you can scrape all this into markdown for your agents! 50% token cost of JSON. We last covered the Inference Inflection in April, and historically most of the advances have been closed source decacorns from Fireworks to Baseten to Modal. Well, time for the open source response. It is day 1 of PyTorch Conf, and DeepSeek timed an intercontinental ballistic missile into the discussion by open sourcing DeepInfer, their production stack, capable of up to 72 hour prompt caching to the 12 hours OpenAI just announced: 🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more… 9:30 AM · Oct 20, 2026 · 3.46M Views This is their customization of vLLM with credit to SGLang and TRRTLLM, but there is a lot added… minus their crown jewels - their ultra optimized KV cache (compact + >8hour lifetime), PD/disggregation, and load balancing (their 3FS was open sourced a while ago). We have translated their blogpost and published…
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