Interactive lesson~18 minAdvanced
Transformer Variants
Transformer variants keep the core attention idea but redesign the cost profile: fewer tokens, smarter kernels, sparse patterns, or distributed context.
Flash AttentionSparseRing Attention
Mental model
The architecture is a budget negotiation between quality, memory, latency, and context length.
Modern systems rarely use plain textbook attention at scale. They use FlashAttention, sparse layouts, sliding windows, and routing tricks.
Latency
balanced68% modeled signal
Memory fit
balanced54% modeled signal
Recall quality
balanced57% modeled signal
Concept pipeline
Build the idea in four moves
Interactive lab
Design an attention stack for a long-document assistant.
Bottleneck
Find whether memory, compute, or communication is limiting.
Focus lens
The part that usually clicks late
FlashAttention
Same exact attention, less memory traffic through tiling.
Latency
68
Memory fit
54
Recall quality
57
Knowledge check
What does FlashAttention primarily optimize?
Next horizon
Where this topic is headed
Paged attention
Block-sparse kernels
Sequence parallelism