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Hunyuan

Tencent
2026-09-11 16:32:27

Tencent open-sources Hunyuan Hy4 preview with 770B parameters and 1 million-token context

Tencent Hunyuan released and open-sourced its next-generation large language model, Hy4 preview, on Aug. 28. The model has 770 billion total parameters, activates 49 billion parameters per token, and supports a context window of more than 1 million tokens. Its weights have been published on Hugging Face under the Apache 2.0 license. According to Xinhua, Tencent positions Hy4 preview as a model built for productivity, targeting software engineering, office analysis, game development, and scientific research. The release adds to a recent run of open model launches by major Chinese technology companies, following Zhipu’s GLM-5.3 and the DeepSeek V4 series. Tencent’s model card says Hy4 preview uses a 78-layer mixture-of-experts architecture, includes FP8 quantization and speculative decoding support, and can be deployed in an example setup using eight GPUs for tensor parallelism. Tencent also disclosed benchmark and internal blind-test results, while noting that third-party independent verification has not yet been completed. The model is available through Tencent Cloud Tokenhub and OpenRouter, and has been integrated into WorkBuddy and CodeBuddy.

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Tencent open-sources Hunyuan Hy4 preview with 770B parameters and 1 million-token context
Tencent
2026-09-01 02:54:47

Tencent's Hunyuan Team Takes a Week Off After Hy4 Preview Release

According to a BlockBeats report, Tencent's Hunyuan team has taken a full week of collective leave after releasing the Hy4 preview. The team's usual workspace, previously occupied almost daily, has been largely empty this week, with the break tied to the latest model release. Hy4 preview was open-sourced on August 28, marking Tencent's largest open-source model to date. It carries 770 billion total parameters, 49 billion activated parameters, and supports a context length exceeding 1 million. In the months before the launch, the team pushed the project forward under sustained high-intensity effort. Once the version went live and won internal approval, the company arranged a structured rest period. In an industry where high-intensity R&D has become standard practice, many model teams tend to move directly into the next iteration after shipping a release. Tencent's decision to insert a centralized pause after reaching a milestone is therefore relatively rare in the industry.

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Tencent's Hunyuan Team Takes a Week Off After Hy4 Preview Release
Tencent
2026-08-28 07:32:42

Tencent Hunyuan Hy4 Preview Overhauls Architecture with DeepSeek Sparse Attention and Zhipu IndexCache

In a flash update on Aug 28, BlockBeats detailed the architectural changes behind Tencent's Hunyuan Hy4 preview. The model is bigger, but the bigger story is under the hood. Hy3 used full attention; Hy4 moves to Gated DSA, a gated form of DeepSeek Sparse Attention. On long inputs, instead of recomputing the entire context, the model selects the most relevant parts and focuses compute there. In Hy3 every layer had to decide what mattered; Hy4 layers Zhipu's IndexCache on top of DSA, so some layers can reuse earlier selections and cut repeated work. Tencent credits DeepSeek and GLM as inspirations. Residual structure also gets a rethink: a standard Transformer is often described as having a single information trunk, while Hy4's iHC widens that to four trunks so the network can retain more information at depth. MoE configuration moves from Hy3's 192 experts to 256 routing experts plus one shared expert, with each token choosing 8 routing experts while always passing through the shared expert.

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Tencent Hunyuan Hy4 Preview Overhauls Architecture with DeepSeek Sparse Attention and Zhipu IndexCache
Tencent
2026-08-26 02:07:21

Tencent executive says computing shortage has slowed Hunyuan AI development

Tencent Senior Executive Vice President and CEO of the Cloud and Smart Industries Group Tang Daosheng has publicly answered criticism that the company has moved too slowly in artificial intelligence. In a long post, he said Tencent has felt internal pressure and acknowledged that rivals have built stronger momentum over the past few years. He also made a direct admission: Tencent’s overall computing capacity has been "seriously insufficient," and that shortfall has already slowed training for the Hunyuan model family as well as product development, with what he described as a fairly large impact. Tang argued that the industry may still be only in the first kilometer of an AI marathon, saying endurance matters more than an early start. He said the next-generation Hunyuan Hy4 will bring more breakthroughs, but added that Tencent cannot rely only on foundation models. In his view, algorithms set the ceiling while engineering determines delivery speed, and Tencent’s strongest advantage lies in real-world scenarios tied to WeChat-based office tools, documents, meetings, and enterprise services. He also reviewed several missteps, including heavy promotion for Yuanbao before the model and product were ready, long-running losses at DevTools, and the uneven early path for CodeBuddy and WorkBuddy. Tang said WorkBuddy later benefited from the Agent wave and shipped more than 40 versions in three months.

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Tencent executive says computing shortage has slowed Hunyuan AI development