#Kv Cache

The DeepSeek team introduced a new approach called Multi-Head Latent Attention (MLA) in their paper for DeepSeek V2, tackling a key bottleneck in LLMs: the size of the Key-Value (KV) cache. In standard transformer architectures, the KV cache stores the key and value vectors for each token in the input sequence. When new tokens are generated, the cache allows the model to efficiently access past information without recomputing it for every new token. This reduces the time complexity, but it increases the space complexity. As the sequence length grows, so does the KV cache, using up loads of memory and leads to slower inference. In MLA, by caching only this compact latent representation instead of the full vectors, DeepSeek reduces the KV cache size by 57x! The model can process much longer contexts and perform inference more efficiently. C: Welch Labs Join our AI community for more posts like this @aibutsimple 🤖 #llm #neuralnetworks #mathematics #math #transformers #computerscience #coding #science #datascience

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You’re in a Senior AI Engineer interview at Amazon, and they hit you with: “KV Caching makes LLMs faster… but what’s the real bottleneck it creates in production?” Most people repeat the textbook answers: - “It prevents recomputing past keys/values.” - “It reduces O(n²) to O(n).” Correct… but you are completely missing the real failure mode. In production The real bottleneck is GPU memory, not compute. KV Caching stores the Key/Value tensors for every token. For long prompts, this cache gets huge. 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Instead of reserving one giant memory block… Break the KV cache into small pieces (“pages”). Only allocate a page when a new token is generated. This is what systems like vLLM do. Dynamic cache management- the idea behind vLLM. You don’t just use KV Caching. You manage it. It lets you: - avoid memory waste - fit many more requests on the same GPU - handle much higher throughput The answer that gets you hired: “KV Caching creates a memory bottleneck because the KV cache is huge and often pre-allocated inefficiently. The production fix is to use dynamic, paged KV storage- like vLLM: so you only allocate memory as tokens arrive, which removes fragmentation and increases throughput.” Save it and Follow for more… #AIEngineering #LLM #MachineLearning #DeepLearning #MetaInterview

In 2023, Meta intern Guangxuan Xiao discovered that removing the first few tokens in a sliding-window KV cache caused catastrophic degradation in long-context LLM performance. These tokens acted as attention sinks, stabilizing attention distributions due to softmax’s requirement that weights sum to one. The simple fix—pinning the first four tokens—enabled models to handle 4M+ tokens without retraining or extra compute, later refined by OpenAI with a “sink scalar” and adopted by HuggingFace, NVIDIA, and others. References: * Xiao, G., et al. StreamingLLM: A Simple Fix for Sliding-Window Attention. MIT HAN Lab Blog, 2025. https://hanlab.mit.edu/blog/streamingllm - OpenAI GPT-OSS Model Card: https://cdn.openai.com/pdf/419b6906-9da6-406c-a19d-1bb078ac7637/oai_gpt-oss_model_card.pdf

People keep obsessing over “bigger models” but the real bottleneck is often memory. Every time you chat with an AI, it has to keep a running notebook called the KV cache, and that notebook lives on GPU memory which is expensive and limited. As the conversation grows, that memory grows too, and that’s one of the reasons longer chats can feel slow or costly at scale. This is why NVIDIA’s KVTC is a big deal. It doesn’t change the model or retrain anything. It just stores that KV cache in a smarter, tighter way so you can run longer conversations and serve more users on the same GPUs. If you watched the reel, tell me what surprised you more. The 20x compression or the fact that the model stays exactly the same. . @nvidia @nvidiaai @nvidiarobotics . . . 🏷️ Day 16, 30 Day Challenge, Generative Al, Artificial Intelligence, Al, Large Language Models, GenAI, Claude, AGI, ChatGPT, Al Evolution, Important Concepts, Series, Al Series Nvidia, KV Cache

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"Explain KV caching in LLMs" 🧠 (a popular LLM interview question) KV caching is a technique used to speed up LLM inference. To understand KV caching, we must know how LLMs output tokens: → Transformer produces hidden states for all tokens → Hidden states are projected to vocab space → Logits of the last token generate the next token → Repeat for subsequent tokens Thus, to generate a new token, we only need the hidden state of the most recent token. How the last hidden state is computed: During attention, the last row of query-key-product involves: → The last query vector → All key vectors Also, the last row of the final attention result involves: → The last query vector → All key & value vectors Key insight: To generate a new token, every attention operation only needs: ✅ Query vector of the last token ✅ All key & value vectors But here's the crucial part: As we generate new tokens, the KV vectors for ALL previous tokens do not change. Thus, we just need to generate a KV vector for the token generated one step before. The rest of the KV vectors can be retrieved from a cache to save compute and time. This is KV caching! To reiterate: Instead of redundantly computing KV vectors of all context tokens, cache them. To generate a token: 1️⃣ Generate QKV vector for the token generated one step before 2️⃣ Get all other KV vectors from the cache 3️⃣ Compute attention KV caching saves time during inference (see video below). In fact, this is why ChatGPT takes time to generate the first token - it's computing the KV cache of the prompt. The tradeoff: KV cache also takes a lot of memory. Consider Llama3-70B: → Total layers = 80 → Hidden size = 8K → Max output size = 4K Here: → Every token takes ~2.5 MB in KV cache → 4K tokens = 10.5 GB More users → more memory. 👉 Over to you: Does KV caching make LLMs more practically useful? #ai #llm #transformers

The DeepSeek team introduced a breakthrough concept called Multi-Head Latent Attention (MLA) in their DeepSeek V2 paper - addressing one of the biggest challenges in large language models: the massive size of the Key-Value (KV) cache). In traditional transformer architectures, the KV cache holds the key and value vectors for every token in the input sequence. This cache allows the model to reuse past computations when generating new tokens, improving speed by avoiding redundant processing. However, as the sequence length increases, the cache grows proportionally - consuming huge amounts of memory and slowing down inference. MLA tackles this by storing a compact latent representation instead of the full key and value vectors. This innovation reduces the KV cache size by an incredible 57x, enabling the model to handle longer context windows and perform inference far more efficiently. C: Welch Labs #llm #deepseek #transformers #deeplearning #machinelearning #AI #neuralnetworks #datascience #computerscience #math #mathematics #artificialintelligence #innovation #AIresearch

It’s actually expected that TTFT is higher: 1️⃣ LLMs Generate One Token at a Time 👉 Output is produced auto-regressively - each new token depends on everything generated before it. There are two parts of token generation: prefill and decode. 2️⃣ Prefill = Slowest Part 👉 The first token triggers the prefill stage: the model must build the entire KV cache from scratch for the whole prompt. No prior computations exist yet, so this step is compute-heavy. 3️⃣ Decode = Much Cheaper 👉 From the second token onward, the model only needs to compute KV tensors for *that* token, not the full sequence. Far fewer operations, far fewer matmuls. 4️⃣ Why This Matters for Latency 👉 Less work → faster decoding → smoother streaming. 👉 TTFT is always slower than the per-token speed that follows. 🏷️ LLMs, AI Engineering, KV Cache, Latency, Token Generation, Performance Optimization Inspiration for the format and making very technical content: my friend @baniascodes :)

LLM Caching explained - KV Cache ( Reddis ) & Semantic Cache #rag #agentic_ai #llm #openai #claude

Cache Control: The HTTP Header that can help make your websites fast #webdevelopment #webdeveloper #fullstackdeveloper #backenddevelopment #backenddeveloper #frontenddevelopment #frontenddeveloper

Stop recomputing the same prompt tokens every request. This is a classic inference inefficiency. The system keeps recomputing attention for identical prompt tokens in every request. Cache the KV states of the shared prompt prefix and reuse them across requests. Here’s the core reasoning👇 The first 2,000 tokens are identical for every request. During inference the model converts those tokens into KV (Key–Value) attention states. Normally this computation happens every time a request comes in, which is why latency and GPU cost keep rising. But those tokens never change, so recomputing them is wasteful. The fix Use Prompt Caching🔥 You process the 2,000-token prefix once, store its KV cache, and reuse that cached state for future requests. Then for each new request the model only processes the ~40 new user tokens instead of the entire prompt. What this achieves: 📍Latency drops significantly 📍GPU compute decreases 📍Throughput increases for high-traffic systems #ai #llm #trendingreels #explorepage #foryou