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DeepSeek on Pulse

The full thread of our DeepSeek coverage — a running record of every development we've tracked, with the VC take on each.

8
Stories
6
With a headline figure
Jun 16, 2026
Since

Coverage Timeline

newest first
  1. Jul 14, 2026IPODeepSeek in Talks to Raise $1.5B, Then IPO$1.5B raise→
  2. Jul 12, 2026AIGoldman Names Its Preferred Chinese AI ModelsPT HK$1,880 (Zhipu)→
  3. Jul 7, 2026AIChina's Open-Weight Models Keep Closing the Gap$98B China chip self-reliance push→
  4. Jun 29, 2026AIDeepSeek and Peking University Open-Source DSpark, Speeding LLM Inference by Up to 85%Up to 85% faster→
  5. Jun 26, 2026AIDeepSeek Open-Sources DSpark, Claiming 60-85% Faster Generation→
  6. Jun 20, 2026AIHuawei-Led Team Post-Trains DeepSeek's 1.6-Trillion-Parameter Model on Ascend Chips, Not Nvidia→
  7. Jun 16, 2026FUNDINGDeepSeek Raises $7.4B -- Becomes China's Most Valuable AI Startup at $50B+$7.4B→
  8. Jun 16, 2026FUNDINGDeepSeek's $7.4B Round Backed by Tencent, CATL and JD$7.4B→

All Coverage

IPO$1.5B raise

DeepSeek in Talks to Raise $1.5B, Then IPO

DeepSeek is reportedly in talks to raise $1.5 billion in private funding as a step toward an eventual IPO, following Goldman Sachs naming it a preferred Chinese AI model.

Jul 14, 2026
AIPT HK$1,880 (Zhipu)

Goldman Names Its Preferred Chinese AI Models

Goldman Sachs research named Zhipu, DeepSeek and ByteDance its preferred Chinese AI models, ranked ahead of Alibaba and Tencent on speed-to-market, and recommended rotating out of Korean AI stocks into a long China AI basket.

Jul 12, 2026
AI$98B China chip self-reliance push

China's Open-Weight Models Keep Closing the Gap

Tencent, Zhipu, Alibaba and DeepSeek are locked in an intensifying open-weight model race, backed by a domestic chip industry drawing roughly $98 billion in spending as China reduces reliance on Nvidia.

Jul 7, 2026
AIUp to 85% faster

DeepSeek and Peking University Open-Source DSpark, Speeding LLM Inference by Up to 85%

DeepSeek and Peking University open-sourced DSpark, a speculative-decoding framework that boosts per-user LLM generation speed by 60-85% -- and up to 661% throughput under tight latency constraints -- without hardware upgrades or model retraining. Released under the MIT license and already live in DeepSeek's V4-Flash and V4-Pro production models, it is another Chinese efficiency breakthrough aimed at slashing the cost of serving AI.

Jun 29, 2026
AI

DeepSeek Open-Sources DSpark, Claiming 60-85% Faster Generation

DeepSeek released DSpark, a 'semi-parallel' speculative-decoding module for its DeepSeek-V4 Flash and Pro models, and open-sourced DeepSpec, a full codebase for training and evaluating speculative-decoding draft models. The company claims generation runs 60-85% faster on Flash and 57-78% faster on Pro at the same throughput -- a fresh reminder that the open-weight challengers keep narrowing the efficiency gap.

Jun 26, 2026
AI

Huawei-Led Team Post-Trains DeepSeek's 1.6-Trillion-Parameter Model on Ascend Chips, Not Nvidia

A research group including Huawei completed full-parameter post-training of DeepSeek's 1.6-trillion-parameter V4-Pro model using a cluster of at least 1,000 Huawei Ascend 910C chips -- demonstrating that a frontier-scale Chinese model can be trained end-to-end on domestic silicon. It's a milestone in China's drive to break free of US chips and the CUDA ecosystem.

Jun 20, 2026
FUNDING$7.4B

DeepSeek Raises $7.4B -- Becomes China's Most Valuable AI Startup at $50B+

DeepSeek closed a $7.4B round backed by Tencent, CATL, JD.com, NetEase and IDG Capital, vaulting past a $50B valuation to become China's most valuable AI startup. The raise is a direct answer to the US frontier labs and a statement that the model race is now a two-superpower contest.

Jun 16, 2026
FUNDING$7.4B

DeepSeek's $7.4B Round Backed by Tencent, CATL and JD

The largest AI financing out of China this year: $7.4B from a strategic syndicate that hands DeepSeek both capital and distribution. At a $50B+ valuation, it is now the anchor of China's frontier-model ambitions.

Jun 16, 2026
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