DeepSeek R2 AI Model Rumors Begin to Swirl Online; Reported to Feature 97% Lower Costs Compared to GPT-4 & Fully Trained on Huawei's Ascend Chips

1.2T param, 78B active, hybrid MoE

That's enormous, very much not local, heh.

Here's the actual article translation (which seems right comparing to other translations):

::: spoiler Translation DeepSeek R2: Unit Cost Drops 97.3%, Imminent Release + Core Specifications

Author: Chasing Trends Observer
Veteran Crypto Investor Watching from Afar
2025-04-25 12:06:16 Sichuan

Three Core Technological Breakthroughs of DeepSeek R2:

1. Architectural Innovation
   Adopts proprietary Hybrid MoE 3.0 architecture, achieving 1.2 trillion dynamically activated parameters (actual computational consumption: 78 billion parameters).
   Validated by Alibaba Cloud tests:

• 97.3% reduction in per-token cost compared to GPT-4 Turbo for long-text inference tasks
  (Data source: IDC Computing Power Economic Model)

1. Data Engineering
   Constructed 5.2PB high-quality corpus covering finance, law, patents, and vertical domains.
   Multi-stage semantic distillation boosts instruction compliance accuracy to 89.7%
   (Benchmark: C-Eval 2.0 test set)

2. Hardware Optimization
   Proprietary distributed training framework achieves:

• 82% utilization rate on Ascend 910B chip clusters
• 512 PetaFLOPS actual computing power at FP16 precision
• 91% efficiency of equivalent-scale A100 clusters
  (Validated by Huawei Labs)

Application Layer Advancements - Three Multimodal Breakthroughs:

1. Vision Understanding
   ViT-Transformer hybrid architecture achieves:

• 92.4 mAP on COCO dataset object segmentation
• 11.6% improvement over CLIP models

1. Industrial Inspection
   Adaptive feature fusion algorithm reduces false detection rate to 7.2E-6 in photovoltaic EL defect detection
   (Field data from LONGi Green Energy production lines)

2. Medical Diagnostics
   Knowledge graph-enhanced chest X-ray multi-disease recognition:

• 98.1% accuracy vs. 96.3% average of senior radiologist panels
  (Blind test results from Peking Union Medical College Hospital)

Key Highlight:
8-bit quantization compression achieves:

• 83% model size reduction
• <2% accuracy loss
  (Enables edge device deployment - Technical White Paper Chapter 4.2) :::

Others translate it as 'sub-8-bit' quantization, which is interesting too.