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Speculative Decoding
Speculative decoding speeds up LLM inference by letting a fast draft model propose tokens that a larger model…
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Low-Rank Adaptation (LoRA)
LoRA fine-tunes LLMs by training small low-rank adapters on top of frozen weights, slashing memory and compute while…
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FlashAttention
FlashAttention is an IO‑aware, exact attention algorithm that tiles work into GPU SRAM and fuses kernels to cut…
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Paged Attention
Paged Attention organizes LLM KV caches into fixed-size pages to reduce fragmentation, enable continuous batching, and support long…
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Vector Database
A vector database indexes high‑dimensional embeddings for fast similarity search with metadata filters and hybrid retrieval—foundational for RAG,…
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Model Quantization
Model quantization reduces precision (e.g., INT8/INT4) for weights and activations to shrink memory and speed inference, enabling cheaper,…
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Key-Value Cache (KV Cache)
A KV cache stores past attention keys/values so LLMs reuse them at each step, cutting latency, enabling continuous…
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Grouped-Query Attention (GQA)
Grouped-Query Attention shares keys/values across groups of query heads, shrinking KV caches and bandwidth to speed LLM inference…
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Speculative Decoding
Speculative decoding speeds up LLM inference by letting a fast draft model propose tokens that a larger model…
-
Low-Rank Adaptation (LoRA)
LoRA fine-tunes LLMs by training small low-rank adapters on top of frozen weights, slashing memory and compute while…
-
FlashAttention
FlashAttention is an IO‑aware, exact attention algorithm that tiles work into GPU SRAM and fuses kernels to cut…
-
Paged Attention
Paged Attention organizes LLM KV caches into fixed-size pages to reduce fragmentation, enable continuous batching, and support long…
-
Vector Database
A vector database indexes high‑dimensional embeddings for fast similarity search with metadata filters and hybrid retrieval—foundational for RAG,…
-
Model Quantization
Model quantization reduces precision (e.g., INT8/INT4) for weights and activations to shrink memory and speed inference, enabling cheaper,…
-
Key-Value Cache (KV Cache)
A KV cache stores past attention keys/values so LLMs reuse them at each step, cutting latency, enabling continuous…
-
Grouped-Query Attention (GQA)
Grouped-Query Attention shares keys/values across groups of query heads, shrinking KV caches and bandwidth to speed LLM inference…