Narrated by Priya Sharma (ElevenLabs Neural Voice)
Voiceover Engine • Interactive Audio Sync
0:0011:00
Learning Objectives & Key Takeaways
0 / 4 Completed- Understand DeepSeek & MoE Architectures core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
Total Training Compute Cost
$5.57M for 671B Params (14.8T tokens)
Verified: KV-cache memory reduced by 93.3%What you will learn
- Multi-Head Latent Attention compresses Key-Value (KV) cache sizes by 93.3% via low-rank latent projections.
- DeepSeek-V3 activates only 37B parameters per token out of 671B total weights, running 256 fine-grained routed experts.
- Trained 14.8 trillion tokens for only $5.57M in GPU compute—shattering traditional frontier model training costs.
Series: Frontier Foundation Model Architectures (Part 2 of 4)
1. The Architectural Anatomy of Multi-Head Latent Attention
Standard Multi-Head Attention (MHA) and Grouped-Query Attention (GQA) store full key-value tensors for every token across layers. In 64k+ context windows, KV cache memory easily eclipses model weights, choking GPU VRAM.
DeepSeek-V3 introduces MLA: instead of caching separate keys and values for each head, it projects the hidden state down into a single compact latent vector c_t of dimension 512, saving 93.3% memory.
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