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

[commander@lemmy.world]

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[brucethemoose@lemmy.world]

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)

2. 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)

3. 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

2. 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)

3. 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.