Source虎嗅•Apr 20, 2026Stalecollected in 16m

DeepSeek Eyes $3B Raise at $10B Valuation

#funding-rumor#nvidia-dependency#chip-migrationdeepseekdeepseeknvidiaptxgrpo

DeepSeek's NVIDIA trap: $10B funding rumor reveals infra shift pains

30-Second TL;DR

What Changed

DeepSeek seeks $3B funding at $10B valuation after rejecting VC a year ago.

Why It Matters

Highlights risks of vendor lock-in for AI startups; funding could aid diversification but performance may suffer short-term.

What To Do Next

Audit your model's GPU optimizations for CUDA/PTX lock-in before scaling inference.

Who should care:Founders & Product Leaders

•DeepSeek seeks $3B funding at $10B valuation after rejecting VC a year ago.
•Delays in V4 due to rewriting PTX-optimized code for non-NVIDIA chips.
•Key talents like Guo Daya, Wang Bingxuan left; heavy NVIDIA GPU reliance was past strength.
•VCs eager to invest, viewing it as founder-driven opportunity despite tech hurdles.

Key numbers$3B$10B

AI-generated analysis for this event — not the original article.

•The pivot toward domestic chip compatibility is part of a broader strategic effort to mitigate risks associated with potential further tightening of U.S. export controls on high-end NVIDIA GPUs.
•DeepSeek's previous rejection of VC funding was rooted in a 'research-first' philosophy, prioritizing internal autonomy over rapid commercial scaling, which has now been challenged by the capital-intensive nature of training next-generation models.
•The departure of key technical personnel is reportedly linked to internal disagreements regarding the trade-offs between pursuing architectural innovation and the immediate necessity of hardware-agnostic optimization.

Primary Architecture

Hardware Focus

Benchmark Focus

Feature	DeepSeek (V4/Projected)	Qwen (Alibaba)	Baichuan AI
Primary Architecture	Mixture-of-Experts (MoE)	Dense/MoE Hybrid	Dense Transformer
Hardware Focus	Transitioning to Heterogeneous	NVIDIA/Custom ASIC	NVIDIA-centric
Benchmark Focus	Reasoning/Coding	General Purpose/Multimodal	Enterprise/Chinese Context

•DeepSeek's V4 architecture relies heavily on a specialized Mixture-of-Experts (MoE) framework designed to reduce compute overhead during inference.
•The technical bottleneck involves the 'PTX-to-Domestic' translation layer, which requires manual kernel rewriting to maintain performance parity with NVIDIA's proprietary CUDA-optimized kernels.
•The model utilizes a custom-developed communication library aimed at reducing latency across clusters of non-NVIDIA GPUs, which currently lacks the maturity of NCCL (NVIDIA Collective Communications Library).

DeepSeek will likely adopt a hybrid cloud-on-premise infrastructure model.

The shift toward domestic chip compatibility necessitates a more flexible deployment strategy to bypass hardware-specific limitations.

The $3B funding round will lead to a significant increase in DeepSeek's commercial API pricing.

Investors will demand a clear path to monetization to justify the $10B valuation, moving away from the previous low-cost or free-tier model.

2023-07

DeepSeek releases its initial open-source model series, establishing a reputation for high-performance research.

2024-01

DeepSeek achieves significant industry recognition with the release of DeepSeek-V2, showcasing advanced MoE capabilities.

2025-02

DeepSeek publicly maintains a stance of independence, rejecting major VC funding offers to preserve research autonomy.

2025-11

Internal reports emerge regarding technical delays in the V4 development cycle due to hardware-software integration challenges.

2023-07
DeepSeek releases its initial open-source model series, establishing a reputation for high-performance research.
2024-01
DeepSeek achieves significant industry recognition with the release of DeepSeek-V2, showcasing advanced MoE capabilities.
2025-02
DeepSeek publicly maintains a stance of independence, rejecting major VC funding offers to preserve research autonomy.
2025-11
Internal reports emerge regarding technical delays in the V4 development cycle due to hardware-software integration challenges.