Project 42
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Module 1 of 3 · 40 min

Navigate the DeepSeek Ecosystem & Architecture

Understand DeepSeek V3/R1 architecture, Multi-head Latent Attention (MLA), DeepSeekMoE, and open-weight availability.

Core concept

By the end

You will be able to

  • Explain Multi-head Latent Attention (MLA) and how it compresses KV cache footprint.
  • Understand DeepSeekMoE architecture with fine-grained expert routing and shared experts.
  • Compare DeepSeek V3 general capabilities with DeepSeek R1 deliberate reasoning behaviors.
01

Architectural Innovations: MLA & DeepSeekMoE

DeepSeek V3 achieves high efficiency through Multi-head Latent Attention (MLA), which compresses keys and values into low-dimensional latent vectors, drastically reducing GPU memory consumption during inference.

DeepSeekMoE activates only a subset of specialized experts per token while maintaining dedicated shared experts for common linguistic knowledge.

DeepSeek V3 Architecture Highlights
text
Total Parameters: 671B
Activated Parameters per Token: 37B (256 routed experts + 1 shared expert)
KV Cache Compression: MLA Low-Rank Projection (93% memory reduction vs standard MHA)

Practice activity

Calculate KV Cache Memory Savings with MLA

  1. Calculate KV cache RAM for 128k context length with standard Multi-Head Attention.
  2. Compute the equivalent memory footprint using Multi-head Latent Attention (MLA).
  3. Document hardware concurrency scalability implications.

What to produce

  • Mathematical comparison spreadsheet of VRAM utilization.

Reflect before continuing

Why is KV cache memory compression critical for serving 128k context windows cost-effectively?

Evidence

Sources and verification

Knowledge check

Make it stick.

Pass at 80%

Choose the strongest answer for each question. Your attempts become part of your account transcript.

01What primary efficiency does Multi-head Latent Attention (MLA) provide during inference?