By Petros Koutoupis, Product Manager, VDURA , Feb 11, 2026 (LinkedIn Blog Post) – AI workflows involve a series of interconnected stages, each with distinct demands on compute, memory, and storage resources. From collecting and preprocessing raw data to training and fine-tuning models, every step requires careful orchestration between GPUs, CPUs, and storage systems. Once models are trained, they perform inference to generate predictions or responses, a process that can be optimized using Key-Value (KV) caching. KV caching improves efficiency by storing and reusing previously computed information, reducing redundant calculations, and accelerating text generation. As AI models grow larger and workloads scale, strategies such as KV cache offloading become essential for maintaining performance and enabling larger, more complex deployments.
A Quick Recap of the AI Workflow
AI workloads span the entire data lifecycle, and each stage places different demands on GPU compute, CPU-based orchestration and preprocessing, storage, and data management. Below are some of the most common workload types you will encounter when building and deploying AI solutions.
Data Ingestion & Preprocessing
Raw data is collected from sources such as databases, IoT devices, social platforms, or APIs. It is then cleaned, filtered, aligned, and formatted so it can be used for analysis and model building.
Model Training
Prepared data is fed into large language models to learn patterns and relationships. This stage is GPU-intensive and requires frequent checkpointing, so training can resume after failures. Additional adjustments or model refinement may occur during this phase.
Fine-Tuning
A pre-trained foundation model is further refined using smaller, domain-specific datasets to improve performance on targeted tasks.
Model Inference
The trained model makes predictions on new data based on what it has learned. For example, shown a new image of a dog, it can identify it as a dog.
KV Caching in a Nutshell
When AI models generate text, they repeatedly perform similar calculations, which can slow down performance. Key-Value (KV) caching accelerates this process by storing and reusing key data from previous steps. Instead of recalculating every token from scratch, the model retrieves cached results, making text generation significantly faster and more efficient.
How Does this work?
Step 1:When the model sees the first input, it computes the high-dimensional key and value vectors that represent the words in context and caches them, so they don’t have to be recomputed later.
Step 2:For each new token, the model reuses the cached keys and values and appends the newly generated ones instead of recomputing everything.
Step 3:It computes attention using the cached K and V together with the new query (Q) to produce the next output.
Step 4:The new token is added to the input stream, and the process repeats until generation is complete.
Comparison between KV Caching and Standard Inference
Here’s how KV caching stacks up against traditional generation:
KV caching significantly improves speed and efficiency, especially for long text generation. By saving and reusing previous computations, it removes the need to recalculate everything for each new token, which results in much faster performance compared to the standard generation process.
KV Cache Offloading
Most inference engines store their KV cache in GPU memory. This creates a tradeoff because using KV caching reduces the compute intensity of the inference workload but requires additional GPU memory. The challenge grows as the size of the KV cache increases in proportion to both batch size (the number of prompts processed at once) and context length (the length of each generation). As context windows expand and inference systems support more users, the KV cache can quickly exceed the available GPU memory.
One way to address this problem is to offload part of the KV cache to CPU memory or storage so the system can scale beyond GPU capacity. This approach is becoming more common as reasoning models and agentic AI gain popularity. The offloading target must be extremely fast to avoid adding significant inference latency. A shared KV cache storage layer that can be accessed across multiple compute nodes in a distributed deployment is also increasingly valuable.
The table below summarizes the advantages and disadvantages of different offload targets:
Tools such as LMCache, NVIDIA’s Dynamo, and several others allow users to offload KV cache from the GPU to other memory resources. By doing so, these tools help reduce KV cache bottlenecks, improve memory efficiency, and enable larger models or batch sizes to run without exhausting GPU memory. Offloading the KV cache can also improve overall inference speed, especially in scenarios where the GPU memory is limited or when running multiple models concurrently.
Conclusion
AI workflows place varying demands on compute, memory, and storage across data ingestion, model training, fine-tuning, and inference. Key-value (KV) caching is an essential optimization for text generation, reusing previously computed information to improve speed and efficiency, especially for long sequences. As models grow and context windows expand, offloading KV cache to CPU memory, local storage, or external storage helps overcome GPU memory limits while maintaining performance. Tools like LMCache and NVIDIA Dynamo streamline this process, reducing bottlenecks, enabling larger batch sizes, and supporting multi-user inference workloads. By combining KV caching with effective offloading strategies, AI systems can scale efficiently and deliver faster, more responsive results in real-world applications.
About the Author
Petros Koutoupis has spent more than two decades in the data storage industry, working for companies which include Xyratex, Cleversafe/IBM, Seagate, Cray/HPE and now, VDURA. In addition to his engineering work, he is a technical writer and reviewer of books and articles on data storage and open-source technologies and has previously served on the editorial board of Linux Journalmagazine.
About VDURA
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