Caching Strategies in LLM Services for both training and inference

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Table of Contents

• Caching Strategies in LLM Services for both training and inference

• Response Caching (LLM Outputs)

  • Response Caching in Chatbots and QA Systems

  • Response Caching in Search Engines

  • Response Caching in Code Generation

• Embedding Caching (Input Features)

  • Embedding Caching in Chatbots and RAG Workflows

  • Embedding Caching in Search Engines

  • Embedding Caching in Code Intelligence

• Key–Value (KV) Caching (Transformer Decoding)

  • KV Caching in Chatbots and Conversational LLMs

  • KV Caching in Long-Form Generation and Code Assistants

  • Training-Time Caching Considerations

• Caching in LLM Training Workloads

• Benchmarks and Industry Impact

Large Language Model (LLM) services use caching at multiple levels to reduce redundant computation and improve latency and cost. The major caching types are response caching, embedding caching, and key–value (KV) caching. We review recent research (2024–2025) and industry practices for each caching type, and highlight use-case scenarios (chatbots, search engines, code generation, etc.) with performance benchmarks.

Response Caching (LLM Outputs)

What it is: Response caching stores previously generated outputs (or partial outputs) for repeated queries. If a new request is identical or semantically similar to a past query, the cached result can be returned instead of recomputing the LLM response. This technique directly cuts down on expensive inference calls and latency.

Why it matters: Real-world LLM applications often see overlapping queries. An industry report noted that roughly 30–40% of LLM requests were similar to previously asked questions (You need more than a vector database - Redis). Caching such repeated queries can save significant cost (one company cited an $80k quarterly OpenAI bill largely from redundant calls ) and improve throughput. Academic studies confirm this prevalence: about 31% of queries to LLMs are repeated exactly or semantically Privacy-Aware Semantic Cache for Large Language Models).

Response Caching in Chatbots and QA Systems

In interactive chatbots and QA systems, users frequently ask variations of the same questions. A straightforward cache keyed by the full prompt can yield immediate hits for identical questions. However, exact-match caches miss rephrased or similar queries. Recent research has therefore focused on semantic caching. MeanCache (2024) introduces a semantic cache that detects if a new query is semantically similar to a past query (using embedding similarity) and then reuses the past answer (Privacy-Aware Semantic Cache for Large Language Models). MeanCache uses a per-user local cache with federated learning to train the similarity model, preserving privacy across users. It demonstrated substantially better cache accuracy – 17% higher F-score and 20% higher precision in cache hit detection – compared to prior caches. By caching semantically similar queries, such systems reduce latency and load without sacrificing answer relevance.

Another approach is Cache-Augmented Generation (CAG), which blurs the line between retrieval and caching. Instead of retrieving documents at query time, CAG preloads a cache of relevant knowledge into the model’s context window and even precomputes the model’s KV-memory for that context (Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks) . For instance, if a chatbot has a fixed FAQ or knowledge base, CAG can initialize the LLM with those documents and cache the model’s internal state. Queries are then answered directly from this enriched context without external lookups. This eliminates retrieval latency entirely . One study showed CAG can match or outperform traditional retrieval-augmented generation on specialized knowledge tasks, removing the real-time retrieval cost while maintaining accuracy .

Industry practice reflects these findings. Semantic caching is becoming a standard in LLM ops to minimize repeated work. For example, engineering blogs recommend caching results keyed by semantic similarity (not just exact string match) to “deliver answers without repeatedly hitting LLMs” (You need more than a vector database - Redis). Open-source frameworks like LangChain provide built-in caching modules: developers can enable an LLM cache (e.g. in-memory or Redis-backed) so that identical prompts return cached responses on subsequent calls. Such caches can be extended with fuzzy matching or embeddings to catch paraphrased queries. In multi-turn assistants, caches may also store partial conversation states or summaries so that if a user revisits a topic, the system can quickly retrieve prior answers.

Response Caching in Search Engines

Search engines and search chatbots (e.g. Bing Chat, academic search assistants) handle massive query volumes where popular queries repeat across users. Caching the LLM’s answer for trending queries can dramatically improve scalability. A 2025 study “Proximity” took this further by caching at the retrieval stage of a search pipeline: it introduced an approximate key–value cache for retrieval-augmented generation (RAG) that reuses retrieved documents when similar queries appear (Leveraging Approximate Caching for Faster Retrieval-Augmented Generation). Instead of hitting the vector database for every user query, Proximity checks if a semantically close query was seen recently and, if so, reuses its retrieved documents for the LLM. This approximate cache improved end-to-end search QA latency significantly – retrieval latency dropped by up to 59% with negligible impact on answer accuracy . By avoiding redundant vector searches, the system lightened database load and sped up responses.

NVIDIA’s Triton Inference Server (an industry-grade serving system) introduced a response caching feature in 2023 that is directly applicable to search/query workloads. Triton computes a hash of the model input tensor and caches the output tensor for future lookups (How to Build a Distributed Inference Cache with NVIDIA Triton and Redis | NVIDIA Technical Blog). If the same query comes in again, Triton returns the cached answer in microseconds from memory, bypassing the model execution . Benchmarks with Triton show huge gains for expensive models: e.g. on a DenseNet model, enabling the cache increased throughput from 80 to 329 inferences/sec (4× speedup) and cut latency from 12.7 ms to 3.0 ms . Even a simpler model saw ~20% throughput gain with caching . These results underscore that response caching yields the biggest wins when each inference is heavy – exactly the case for large LLMs in search. Triton’s cache can be local or backed by distributed stores like Redis, and tests found a distributed Redis cache performed on par with local caching (within ~8% latency difference) . This means search engines can cache responses across a cluster of inference servers effectively, enabling cache sharing and higher hit rates.

Response Caching in Code Generation

For LLM-based code generation (e.g. code assistants in IDEs or documentation Q&A), caching is less straightforward since prompts often contain unique code context. However, even coding assistants encounter repeated patterns (common library questions, boilerplate prompts, etc.). Micro-caching techniques have been adopted to exploit this. For example, Anima’s frontend code assistant hashes each code-generation request (including the problem description and context) and stores the resulting code snippet. If an identical request or snippet batch is seen again, the cached output is returned immediately (Minimizing LLM latency in code generation - Anima Blog). This “micro-cache” helped their system handle multiple parallel code completions with a more powerful (but slower) model, effectively trading space for speed . The engineering team reported that batching plus caching was key to using a smarter LLM without sacrificing latency . In practice, code assistants could cache solutions for common tasks (e.g. “write a Python quicksort function”) – much like how Stack Overflow serves cached answers – and thereby avoid re-generating well-known code.

Response caching is also useful in the context of AI coding Q&A sites or forums. If an LLM backs an “Ask a coding question” feature, many users may ask very similar things (e.g. “How do I fix error X in library Y?”). A semantic cache could recognize previously answered questions and surface the cached answer (with minimal regeneration). This not only reduces compute cost but also ensures consistency (the same question yields the same answer, rather than stochastic variations).

Challenges: A major challenge in response caching is validation – ensuring that a cached response is still valid for the new query. For exact repeats, it’s trivial, but for semantically similar queries, one must verify that the answer fits the new phrasing or context. Techniques like semantic equivalence checking or answer embedding similarity are used to mitigate false hits (Privacy-Aware Semantic Cache for Large Language Models). Another challenge is cache freshness: if the LLM or underlying data is updated, cached answers might become outdated or reflect an old model. In practice, caches may be invalidated on model updates or use time-to-live for responses. Despite these, 2024/2025 trends show response caching (especially semantic caching) becoming a staple in LLM service optimization for chatbots, search, and even code assistants.

Embedding Caching (Input Features)

What it is: Embedding caching stores vector representations (embeddings) of inputs or documents so they don’t need to be recomputed repeatedly. In LLM pipelines, this often applies to text embeddings used for retrieval or semantic comparisons. For example, in a RAG system, you may cache the embeddings of frequent queries and of document corpus chunks. Similarly, caching intermediate embeddings (like encoder outputs) during training can avoid redundant forward passes.

Why it matters: Computing embeddings (via BERT, sentence transformers, etc.) can be nearly as expensive as running the LLM itself, especially at scale. Caching them saves time on repeated computations and ensures consistent results. Moreover, many real-world systems incorporate vector search – which relies on embeddings – into their workflows (e.g. semantic search, clustering, recommendation). Caching becomes crucial for those to operate efficiently under load.

Embedding Caching in Chatbots and RAG Workflows

Many chatbots augment their capabilities by retrieving external knowledge (documents, FAQs) based on the user query embedding. In these retrieval-augmented generation scenarios, caching is used at two levels: (1) caching document embeddings, and (2) caching query embeddings or retrieval results. Document embeddings (for the knowledge base) are usually precomputed offline and stored in a vector index – effectively a persistent cache. For query embeddings, systems can keep a short-term cache: if the same user asks the same question twice, the second time the query embedding lookup can be skipped, directly reusing the stored vector. Beyond exact repeats, semantic caching can apply here as well (similar query strings yielding the same or nearby embedding). The Proximity system discussed earlier is one example – it caches not the final answer but the retrieved docs for similar queries (Leveraging Approximate Caching for Faster Retrieval-Augmented Generation), which is an embedding-level cache of the query->document mapping. By doing so, it reduced expensive nearest-neighbor searches by ~59% latency .

Another use in chatbots is caching user-specific embeddings (like a user profile or conversation summary). For instance, a long conversation may be distilled into an embedding that represents the user’s preferences; caching that avoids recomputing it for each new session. Some personalized LLM services embed user history or persona and reuse that vector when the user returns.

Embedding Caching in Search Engines

For semantic search engines (which use vector similarity to find results), embedding caching is essential. Search systems often face repeated queries: popular search terms or questions that many users independently ask. Rather than compute a fresh embedding for each occurrence, the search backend can cache the query string to embedding mapping. Likewise, if the index is updated, re-embedding all documents from scratch is costly – so many pipelines cache embeddings and only update incrementally for new or changed documents.

The importance of embedding caching in production is highlighted by LLMOps frameworks. The Redis AI platform, for example, explicitly recommends “caching embeddings to avoid re-embedding the same chunk of data repeatedly.” (You need more than a vector database - Redis). This is implemented in their Redis Vector databases where recently used embedding vectors can be stored in memory for quick reuse. Similarly, LangChain provides a CacheBackedEmbeddings wrapper, which hashes the input text and stores the resulting embedding in a key–value store (Caching | ️ LangChain). On subsequent calls with the same text, LangChain retrieves the cached vector instead of calling the embedding model again . This mechanism can be applied to both document embeddings (e.g. caching vector of a document chunk) and query embeddings. By hashing the text content, it ensures that identical text yields a cache hit, saving the cost of a forward pass through the embedding model. In practice, developers use stores like Redis, SQLite or in-memory dictionaries for these embeddings caches, achieving significant speed-ups in high-QPS applications.

Approximate caching: An interesting 2025 research direction is using approximate matches for embedding cache hits. Since no two user queries are exactly the same, one can cache embeddings for “similar” texts. For example, if a user searches “how to tune hyperparameters in BERT” and later another asks “tips for BERT hyperparameter tuning”, their embeddings will be close. A system could detect this and reuse the nearest cached embedding instead of recomputing from scratch. While this introduces a small error, it can boost performance. Proximity’s approach of reusing documents for similar queries (Leveraging Approximate Caching for Faster Retrieval-Augmented Generation)is one form of this idea. Another (hypothetical) approach could maintain a cache of common sub-sentences or n-grams embeddings to compose new ones. However, care is needed to balance speed vs accuracy.

Embedding Caching in Code Intelligence

In code search or code generation contexts, embedding caching also appears. For example, a code search engine that lets developers find similar code snippets (using embeddings of code) would cache embeddings of the source code files to avoid recomputation on each search. If the same file is queried repeatedly (or multiple similar queries on the same file), a cached embedding drastically reduces latency.

For LLM-based code assistants, embedding caching might come into play when retrieving relevant documentation or examples. Suppose a code assistant frequently looks up API documentation paragraphs based on the coding question – caching those doc embeddings in memory means subsequent similar questions can skip re-embedding the same paragraphs. This is analogous to document caching in text domain. While research specifically on caching embeddings in code tasks is sparse, the concept is similar: identify repeated pieces of text/code and avoid duplicate encoding work.

Performance impact: Embedding caching primarily saves computation time on the encoder models. If an embedding model takes 100 ms per text and you cache and reuse results for even 20% of queries, that’s a direct 20% reduction in average latency for the retrieval step. The 2025 Proximity work demonstrated nearly 60% end-to-end speedups in RAG by clever reuse of retrieval results (Leveraging Approximate Caching for Faster Retrieval-Augmented Generation). Another benefit is consistency – using a cache ensures the same text always yields the same embedding (avoiding rare nondeterminism in some models), which can make the downstream results more stable.

In summary, embedding caching is a relatively straightforward but high-impact strategy in LLM services that involve retrieval or similarity search (common in search engines and knowledge-enhanced chatbots). Framework support and recent research both emphasize caching at this stage as low-hanging fruit for optimization.

Key–Value (KV) Caching (Transformer Decoding)

What it is: KV caching refers to storing the key and value tensors from each Transformer attention layer for past tokens, so that on the next token generation the model can reuse these instead of recomputing attention over all prior tokens. In plain terms, KV caching lets an autoregressive LLM “remember” the intermediate states for the tokens it has already generated (or processed) and append new tokens with O(n) work instead of O(n²).

Why it matters: Modern decoder-only LLMs generate tokens sequentially, and naive implementation would recompute all self-attention calculations from scratch for every new token. KV caching avoids that repetition, yielding dramatic speedups for long outputs. It is practically mandatory for any high-performance LLM inference. As a concrete example, enabling KV cache in Hugging Face Transformers made generation ~5× faster for a 300-token output on a T4 GPU (11.7 s vs 61 s without cache) (KV Caching Explained: Optimizing Transformer Inference Efficiency). The trade-off is memory – the cache consumes GPU memory proportional to sequence length. Nonetheless, for latency-critical use cases like chat and streaming generation, the speed gain is indispensable.

KV Caching in Chatbots and Conversational LLMs

Interactive chatbots rely heavily on KV caching to achieve real-time responsiveness. Each user query plus the chatbot’s prior responses can amount to thousands of tokens of conversational context that the model conditions on. With KV caching, the initial processing of the prompt (often called the “prefill” stage) computes and stores the keys/values for the entire context. Then as the chatbot generates its answer token by token, it reuses those cached keys/values instead of recalculating attention over the whole history each time. This allows the per-token compute cost during generation to stay roughly constant even as the conversation grows longer (KV Caching Explained: Optimizing Transformer Inference Efficiency). Without caching, latency would balloon for long dialogues, making fluid conversation impossible.

Hugging Face engineers note that KV caching is “especially useful for long texts” since it keeps generation time linear rather than superlinear in length . In practice, all major LLM serving frameworks (Transformers, FasterTransformer, TextGenerationInference, etc.) enable caching by default for text generation. KV caching is also what enables streaming APIs: the model can output tokens one-by-one to the user while internally using the cache to append tokens efficiently.

One specific scenario in multi-turn chats is reusing caches across turns. If the model architecture and serving stack allow, after the model generates its answer, one could retain the KV cache for the entire dialog (user query + model answer). When the user asks the next question, one could append it to the cached state instead of starting from scratch. Implementing this is non-trivial, because the new user question must also be processed and its keys/values added – but some inference servers support partial cache reuse. The vLLM/SGLang inference engine introduced a “cache-aware load balancer” that routes chat sessions to servers likely to have their context prefix cached (SGLang v0.4: Zero-Overhead Batch Scheduler, Cache-Aware Load Balancer, Faster Structured Outputs | LMSYS Org). By predicting prefix cache hits, it achieved up to 1.9× higher throughput in multi-user chatbot serving . Essentially, if many users share a system prompt or initial conversation state, keeping those on a particular worker’s cache and directing similar sessions there yields huge efficiency gains (their cache hit rate improved 3.8× with this method ).

KV Caching in Long-Form Generation and Code Assistants

For applications like document summarization, story generation, or code generation, the outputs can be very long (hundreds or thousands of tokens). KV caching is absolutely critical in these scenarios to maintain reasonable inference time. A case in point: code completion might generate a large block of code from a given prompt. With KV caching, the model can generate each new line by leveraging the stored state of prior code lines, rather than recomputing the entire file context repeatedly. This makes advanced code assistants viable even on local hardware.

However, long outputs stress the cache memory. It’s documented that KV cache size grows linearly with sequence length and can even surpass the model weights in memory usage (Anchor Attention, Small Cache: Code Generation with Large Language Models). For example, a 7B parameter model requiring 14 GB for weights may need an additional 16 GB for KV cache at 32 batch × 1024 sequence length . This memory bloat can reduce batch size and throughput if not managed (INT4 Decoding GQA CUDA Optimizations for LLM Inference | PyTorch). To address this, recent research has introduced KV cache compression and layer-wise selective caching:

• Quantizing KV: Both academia and industry explored reducing precision of the cache. A PyTorch team applied INT4 quantization to Llama-2’s KV cache, finding it preserved accuracy close to BF16 . Hugging Face reported that using 4-bit (INT4) KV cache yields about 2.5× memory savings with minimal perplexity change (Unlocking Longer Generation with Key-Value Cache Quantization) . Their tests on long-context benchmarks showed INT4 cache had virtually the same quality as FP16, whereas more aggressive INT2 caused noticeable loss . The speed impact of INT4 was small at moderate batch sizes, though at very large batch some overhead appears . Overall, KV quantization is a promising way to unlock longer generations on limited GPU memory . The trade-off is a potential slight slowdown, especially if combined with weight quantization (which in one experiment led to a 3× speed decrease for a fully quantized pipeline) . Still, for many, the memory relief is worth a minor speed hit.

• Selective cache eviction: Not all past tokens contribute equally to the next token’s prediction. 2024–2025 research has proposed evicting or downsampling less important tokens’ KV pairs to shrink memory. XKV (2024) observed that later layers of a Transformer are more sensitive to long-context retention than earlier layers, and that a one-size-fits-all cache eviction per layer is suboptimal (XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference) . They formulated an optimal layer-wise cache allocation problem, allowing some layers to drop more tokens from the cache than others. By “personalizing” the cache size per layer, XKV cut KV memory usage by 61.6% on average and improved throughput by up to 5.2× with negligible accuracy loss . Similarly, SqueezeAttention (ICLR 2025) allocates cache budget in two dimensions: across tokens (sequence length) and across layers. It identifies which layers can tolerate aggressive pruning and which cannot, then compresses the cache accordingly. This joint 2D optimization yielded 30–70% memory reduction and up to 2.2× throughput gain on various LLMs (SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget | OpenReview). In short, these techniques “squeeze” the KV cache by removing redundancy, while keeping model output quality almost intact.

• Anchor-based caching (for code): Code generation has its own quirks – often, certain anchor tokens (like punctuation, delimiters, or specific syntax tokens) carry the bulk of long-range dependencies. A 2024 work on Anchor Attention introduced a method to compress KV cache for code by focusing on these anchor points (Anchor Attention, Small Cache: Code Generation with Large Language Models). Their AnchorCoder approach achieved at least 70% KV cache size reduction in code LLMs with minimal performance drop . By storing high-information tokens with full fidelity and compressing the rest, it managed the context more efficiently. This is especially useful for code assistants dealing with files or functions where a lot of the earlier content (e.g. comments or boilerplate) is less relevant to later code generation.

In industry deployment, these advanced cache management strategies are starting to appear in inference engines. For example, Meta’s Llama 2 model uses Grouped-Query Attention (GQA), which effectively shares one KV cache across multiple attention heads (INT4 Decoding GQA CUDA Optimizations for LLM Inference | PyTorch). By grouping heads, GQA reduces the number of KV tensors, cutting memory usage without significant quality loss. PyTorch’s optimization on Llama-2 leveraged GQA and INT4 together: after specialized CUDA kernels, their INT4 GQA implementation actually outperformed FP16 (it was 1.4–1.7× faster than FP16 on A100/H100 GPUs) . This shows that carefully engineered low-precision caches can even improve throughput by better utilizing memory bandwidth .

For serving many users or sessions in parallel, KV cache management becomes a systems design issue. We already touched on cache-aware request routing (to maximize prefix reuse). Another angle is elastic cache offloading: moving old cache entries to CPU or disk when GPU memory is tight, then bringing them back if needed for context. Techniques like PagedAttention (proposed in 2023) treat KV cache like virtual memory pages, swapping them in/out. A 2025 system, HybridServe, balances recomputation vs caching in an offloading scenario (Efficient LLM Inference with Activation Checkpointing and Hybrid Caching). It uses activation checkpointing (storing a smaller “activation cache” that can regenerate KV on the fly) to hide CPU-GPU transfer latency. By mixing actual KV caching with on-demand recomputation from lighter checkpoints, HybridServe achieved a 2.19× throughput improvement over prior offloading approaches . This is highly relevant for cost-driven deployments where one might use a CPU memory extension to serve very long contexts or many concurrent sessions on fewer GPUs.

Training-Time Caching Considerations

While KV caching is primarily an inference mechanism, note that during training (especially causal language modeling training), we typically feed full sequences in parallel rather than generate token by token, so there is no analogous incremental cache – the model sees each sequence in one shot. Instead, training optimizations involve techniques like activation checkpointing (which forgoes caching activations to save memory and recomputes them in backward pass). Thus, KV caching as discussed is not used in standard training, but appears in specialized fine-tuning scenarios (e.g. reinforcement learning with generation or sequential decoding during training loops).

However, training does benefit from other caching forms. For instance, data caching is crucial when training on huge corpora: frameworks cache preprocessed dataset shards in memory or on local SSD to avoid slow data loading each epoch (Handling Large Datasets in LLM Training: Distributed Training Architectures and Techniques). Pipeline caching ensures that tokenized text or augmented data (with prompts) don’t get recomputed every time. If training involves retrieving documents (as in RAG fine-tuning), caching those retrieval results or document embeddings (similar to inference) can significantly accelerate each training step.

In summary, KV caching is a cornerstone of efficient LLM inference, especially for chatbots, long-form generation, and coding assistants. It provides the massive speedups that enable real-time interaction with large models. The latest developments focus on mitigating its memory costs – via quantization, smarter eviction, and system-level orchestration – so that even longer contexts and larger batches can be served cost-effectively. These caching improvements directly translate to user-facing benefits: lower latency per token and the ability to handle longer prompts or outputs without blowing up GPU memory. As one Hugging Face report aptly put it, “KV caching makes a big difference in speed and efficiency, especially for long texts” (KV Caching Explained: Optimizing Transformer Inference Efficiency) – a statement consistently borne out by both research and industry benchmarks in 2024–2025.

Caching in LLM Training Workloads

Although most caching discussions target inference, there are caching strategies that assist LLM training as well, primarily around data handling:

• Dataset caching: Large-scale LLM training involves reading terabytes of text. I/O can become a bottleneck if each training process constantly loads data from remote storage. To address this, training pipelines use caching at the data layer. One common practice is to cache tokenized datasets or shards on local disk or memory after the first epoch. For example, the Hugging Face Datasets library allows caching processed data files so that subsequent epochs or runs reuse the cached binary files. Similarly, distributed training setups use data sharding and caching – splitting the data and pre-loading shards into each worker’s memory to avoid network reads (Handling Large Datasets in LLM Training: Distributed Training Architectures and Techniques). A tech report (2024) on handling large datasets notes that caching data in memory “avoids needing to reload data from storage” repeatedly, which is critical when scaling to many machines . This reduces training time variability and keeps GPUs fed with data.

• Feature caching: In certain multi-stage training, one might freeze some model components and cache their outputs. For example, if doing sequential training (say first train a transformer encoder, then use it in another model), one could cache the encoder’s outputs for the training set to use in the second stage. In LLM fine-tuning, if some preprocessing like embedding or inference is needed per example (e.g. computing a retrieval for a training query), those results can be cached to disk to avoid re-computation each epoch. This is analogous to embedding caching but on the training side.

• Micro-batching cache: Large batch training often splits batches into micro-batches. Frameworks sometimes cache intermediate results between micro-batches (like the computed loss scaling factors or partial gradients) to aggregate later. While not commonly referred to as caching, gradient accumulation buffers serve a similar role (storing partial sums).

Interestingly, ideas from inference caching are also being considered for training efficiency. A recent study proposed a KV-activation hybrid cache to speed up model weight offloading during training/inference by caching some activations so that not every step requires recomputing from scratch (Efficient LLM Inference with Activation Checkpointing and Hybrid Caching). While aimed at inference, similar logic could apply if one were training with model parallelism and needed to swap layers in/out of GPU – caching activations could reduce recomputation overhead.

Overall, caching in training is about eliminating redundant data transformations. While training doesn’t reuse outputs in the same way inference does, it does reuse data across epochs and across processes – a prime opportunity for caching. Ensuring each piece of data is processed (tokenized, encoded, etc.) once and then reused can save a lot of time when training over billions of tokens.

Benchmarks and Industry Impact

Empirical results across research papers and industry benchmarks consistently show that caching strategies can dramatically reduce latency and cost in LLM services:

• Enabling KV caching yields 5× or greater speedups for long sequence generation (KV Caching Explained: Optimizing Transformer Inference Efficiency), turning what was once a quadratic-time operation into linear time. This makes the difference between a chatbot taking one second to respond versus five seconds – a huge gain in user experience. It also enables scaling to longer contexts (e.g. 8K, 32K tokens) which would be infeasible to handle without caching. As one summary put it, “stays fast even with longer texts by avoiding repeated work” .

• Response caching and semantic caching can cut down repeated query handling by 30% or more, directly saving on inference compute. MeanCache’s approach to semantic hits would improve cache utility by ~17% in accuracy, meaning more queries served from cache and fewer expensive LLM invocations. The Redis team’s anecdote of a 4× cost reduction potential by caching 30–40% repeated questions illustrates why companies are eagerly adopting these caches (You need more than a vector database - Redis) .

• In a multi-user LLM service, intelligent cache utilization has a multiplicative effect. For instance, SGLang’s cache-aware load balancing nearly doubled throughput (82665 to 158596 tokens/s) when scaling to 8 workers (SGLang v0.4: Zero-Overhead Batch Scheduler, Cache-Aware Load Balancer, Faster Structured Outputs | LMSYS Org). This shows that caching not only speeds up single requests but also improves parallelism and overall throughput in a production server setting.

• Caching also contributes to stability and consistency, which are harder to quantify but crucial in production. By reusing known-good outputs for identical inputs, services ensure deterministic answers for repeated queries (useful for auditing and user trust). And by reducing load, caching prevents latency spikes during traffic bursts (since repeated queries hit cache instead of queuing for the model).

From framework support (Hugging Face Transformers, Triton, LangChain, etc.) to cutting-edge research (ICLR 2025 papers on KV optimization), the trend is clear: leveraging cache at every possible layer of the LLM stack is key to making large models practical and scalable. Companies like OpenAI and Google closely guard their infrastructure details, but it’s reasonable to assume they heavily employ caching (e.g. ChatGPT likely caches popular Q&A pairs and uses KV caching internally). Open-source implementations are rapidly catching up, integrating these ideas so that any team deploying an LLM can benefit from state-of-the-art caching by default.

In conclusion, caching strategies — from response-level memoization to embedding reuse and transformer KV caches — have proven to be game-changers for LLM services in 2024 and 2025. They attack the redundancy inherent in LLM workloads: repeated text in inputs, repeated queries, and repeated calculations in autoregression. By eliminating duplicate work, caching slashes latency, boosts throughput, and reduces the cost per query. The latest research continues to push caching efficiency (smarter eviction, compression, and routing), while industry frameworks make these techniques increasingly accessible. For AI engineers, a deep understanding of caching mechanisms has become essential to maximize LLM deployment performance and achieve cost-effective, real-time AI services. (KV Caching Explained: Optimizing Transformer Inference Efficiency)