DeepSeek V4.1-Flash, Explained Like You’re New: KV Caches, MoE, and the 890-Byte Trick
DeepSeeded? No — DeepSeek. In September 2026 the Chinese AI lab released a paper titled “DeepSeek-V4.1-Flash: Pushing the Limits of
Tag: LLM
DeepSeek-V4.1-Flash: Why the Most Interesting AI Paper This Month Is About Storage
Serving a large language model to thousands of concurrent users is, underneath all the marketing, a memory management problem. Every
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Speculative Decoding Explained: How EAGLE-3 Makes LLMs 2-3x Faster Without Changing Outputs
Autoregressive decoding is the reason large language models feel slow: every token is generated by a full forward pass, and
PagedAttention and the KV Cache: How OS Paging Explains Modern LLM Serving
When an LLM serves a 2,000-token response to a prompt of 10,000 tokens, it does something that looks absurd from
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RAG Chunking Strategies in 2026: What the Benchmarks Actually Show
Every RAG system has two halves: a retriever that decides which text the model sees, and a generator that answers
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LLM Routing and Cascading: Send Every Query to the Cheapest Model That Can Handle It
If you run a large language model behind a product, you have probably internalized an uncomfortable trade-off. The big frontier
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LLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
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Speculative Decoding: Getting 2-3x More Out of Every GPU Pass
Watch a GPU while a large language model generates text and you’ll see something strange: for most of every forward
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vLLM Prefix Caching: The Prefill Optimization You’re Already Running
Every request that hits an LLM server pays the same tax before the first generated token appears: the prompt must
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Constrained Decoding: How Structured LLM Output Actually Works
Ask an LLM for JSON and you will usually get JSON. “Usually” is the word that ruins your week. One
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