⚡ What DeepSeek-V4.1-Flash teaches us about efficient AI

Q: DeepSeek V4.1-Flash is a 552B MoE model with 890 bytes per token KV cache, 4x lighter than V4-Flash

DeepSeek V4.1-Flash is a 552B MoE model with 890 bytes per token KV cache, 4x lighter than V4-Flash and 1/8th the SSD cache storage.

Q: CSA2 sparse attention lets layers share KV and retrieval results, while a Hierarchical Sparse Indexe

CSA2 sparse attention lets layers share KV and retrieval results, while a Hierarchical Sparse Indexer bounds search costs to a 2,048-block candidate pool.

Q: DeepSeek V4.1-Flash activates only 8B params per input token and 16B per output token via its Causal

DeepSeek V4.1-Flash activates only 8B params per input token and 16B per output token via its Causal Encoder-Decoder architecture.

·Sunday, September 13, 2026·8 min read

AI/ML Engineering Technology

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AI Summary

DeepSeek released V4.1-Flash, a 552B MoE model that is nearly twice the size of V4-Flash but uses 4x less KV cache per token. Architectural changes including Causal Encoder-Decoder, Sliding-Window Attention with Bounded Replay, and Compressed Sparse Attention 2 reduce memory and serving costs for long-context agent workloads. The model scores 40 on the Artificial Analysis Intelligence Index, behind Gemini 3.8 Flash High's 41, at a quarter of the cost per task.

Key Facts

✓DeepSeek V4.1-Flash is a 552B MoE model with 890 bytes per token KV cache, 4x lighter than V4-Flash and 1/8th the SSD cache storage.

✓CSA2 sparse attention lets layers share KV and retrieval results, while a Hierarchical Sparse Indexer bounds search costs to a 2,048-block candidate pool.

✓DeepSeek V4.1-Flash activates only 8B params per input token and 16B per output token via its Causal Encoder-Decoder architecture.

Author Takes

Neutral

LLM parameter count vs serving cost

Parameter count alone says less about production cost; efficiency depends on active params per token, KV cache bytes, persisted state, and retrieval cost.

Bullish

Future efficient AI models

Next-generation efficient models might not have fewer parameters; they'll get better at deciding which parameters run, which memories survive, and which parts of a million-token history are worth revisiting.

⚡ What DeepSeek-V4.1-Flash teaches us about efficient AI

AI Summary

Key Facts

Author Takes

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