Observed Signal · Feb 13, 2026 · Technical Release · Source: AI Supremacy · Impact: 3/5 · Sentiment: Positive
DeepSeek V4 (MODEL1) Expected with Engram, mHC
DeepSeek, a Chinese open-source AI startup, is expected to release DeepSeek V4 (rumored codename MODEL1) around the Lunar New Year (week of Feb 17, 2026). Reporting and code commits indicate V4 will be a major architectural overhaul focused on extreme long-context coding and software-engineering tasks. Key innovations described include Engram (a conditional memory lookup to separate factual recall from reasoning and enable multi-million-token knowledge stores), Manifold-Constrained Hyper-Connections (mHC) to stabilize rich cross-layer connectivity, and DeepSeek Sparse Attention (DSA) for 1M+ token contexts. DeepSeek reportedly delayed its R2 training after hardware instability with Huawei Ascend chips and reverted to Nvidia GPUs for final training. The article places DeepSeek within a broader surge of Chinese open-weight model activity (names cited include Qwen/Alibaba Cloud, Zhipu AI, Moonshot AI, and Minimax).
A significant technical release from a leading open-source Chinese AI lab could narrow the gap with proprietary LLMs, introduce new long-context and memory architectures relevant to developer tooling and martech, and signal accelerating Chinese open-weight model innovation; hardware training issues also highlight geopolitical and supply-chain implications for model training.
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Key Takeaways & Evidence Grounding
- DeepSeek is expected to launch DeepSeek V4 (rumored codename MODEL1) around the Lunar New Year (week beginning February 17, 2026).
- DeepSeek V4 is described as an architectural overhaul introducing Engram (a conditional memory lookup enabling very large knowledge stores and >1M-token effective context).
- DeepSeek introduced Manifold-Constrained Hyper-Connections (mHC) to stabilize rich cross-layer connectivity and reduce training instability.
- DeepSeek Sparse Attention (DSA) and other attention optimizations aim to support 1M+ token context windows and long-context coding tasks.
- DeepSeek reportedly encountered training instability when attempting to train R2 on Huawei Ascend chips and reverted to Nvidia hardware, delaying R2.
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DeepSeek previews V4 open-source LLM
Deepseek on April 24, 2026 published its long‑anticipated Deepseek V4 (variants Pro and Flash), an open‑source large language model built on a new architecture with 1.6 trillion parameters. The company highlights significant gains in reasoning and autonomous code generation, claims benchmark-leading performance in mathematics, STEM and programming among open models, and says V4 supports context windows up to one million tokens while reducing compute and memory costs. Deepseek positions V4 Pro as materially cheaper on coding tasks versus OpenAI’s GPT‑5.5. The rollout also involves a partnership with Huawei, which supplies "Supernode" clusters of Ascend‑950 chips; Deepseek and analysts note a strategic focus on Huawei and Cambricon domestic chips to relieve reliance on Nvidia/AMD. Market reaction is expected to be more muted than Deepseek’s earlier 2025 breakthrough R1 shock.
DeepSeek in talks to raise $1.5B, pursue IPO
DeepSeek, a China-based large language model developer founded in 2023, is reportedly negotiating to raise about $1.5 billion at roughly a $71 billion valuation and is preparing for an IPO targeted for 2027 (with the possibility of an earlier debut). The report follows a $7 billion funding round closed roughly a month earlier at about a $50 billion valuation. In June, DeepSeek accounted for nearly 23% of tokens processed by enterprise-focused AI gateway Vercel, versus Anthropic's 32%. The company runs its cloud service on chips made by Huawei Technologies. Reported investors include Tencent and Beijing’s National Artificial Intelligence Industry Investment Fund. The story was reported by Bloomberg and summarized by TechCrunch on 2026-07-14.
DeepSeek Founder: Compute Is the Primary Constraint
Hello China Tech published a July 2026 selection from a nearly four-hour investor transcript in which DeepSeek founder Liang Wenfeng argued that compute availability is the principal gap between DeepSeek and leading US AI labs. Liang framed differences in talent, model capability, and applications as downstream consequences of smaller compute budgets and limited chip supply. He placed DeepSeek’s first external round at over Rmb 50bn (~$7.4bn) (not officially confirmed by the company), described work to reduce dependence on Nvidia via a high-level compiler called TileLang, and forecast that within a year domestic Chinese chips could be verified as usable for training. Liang also discussed pricing, open-weight releases, team retention via option grants, and V4 multimodality commitments.
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