Introduction
Large Language Models (LLMs) have become the backbone of modern natural language processing, powering chatbots, content generators, and even complex decision‑making systems. Yet the sheer size of these models—often running into billions of parameters—creates a formidable barrier when it comes to deploying them in real‑time, resource‑constrained environments. The most common bottleneck is the repeated computation of attention scores for every token in a sequence, which can quickly become a performance nightmare.
Enter the KV cache, a deceptively simple yet powerful optimization that turns the attention mechanism from a brute‑force engine into a highly efficient lookup system. By storing the key and value tensors that the model has already computed for a given context, the KV cache allows subsequent tokens to reuse this information without re‑executing the expensive transformer layers. The result is a dramatic reduction in both latency and GPU memory usage, making it possible to serve LLMs to thousands of users with a single server.
In this post we will unpack the mechanics of KV caching, walk through a minimal implementation from scratch, and explore how this technique can be leveraged to build production‑ready inference pipelines. We’ll also touch on emerging research directions that aim to push the boundaries of what a cache can do.
Understanding KV Caches
The transformer architecture relies on self‑attention, where each token in a sequence attends to every other token. Mathematically, the attention score between a query vector “q” and a key vector “k” is computed as a dot product, scaled by the square root of the dimensionality, and then passed through a softmax to produce a weight. This weight is multiplied by the corresponding value vector “v” to produce the output for that token.
During inference, the model processes tokens one at a time. For the first token, the entire key and value space is empty, so the model must compute the keys and values for that token from scratch. For the second token, the model must recompute the keys and values for the first token again, even though they have not changed. This redundancy grows linearly with the length of the sequence, leading to a quadratic time complexity in the worst case.
A KV cache sidesteps this issue by persisting the keys and values for all tokens that have already been processed. When a new token arrives, the model only needs to compute its own key and value; the rest of the attention computation can reuse the cached tensors. The cache is typically organized as a two‑dimensional tensor where one dimension indexes the sequence position and the other indexes the hidden dimension. The cache is updated incrementally: each new token appends its key and value to the existing tensors.
The benefits are twofold. First, the computational cost per token drops from “O(n^2)” to “O(n)” because the expensive dot products are performed only against the new query vector. Second, memory consumption is reduced because the model no longer needs to store intermediate activations for the entire sequence; it only keeps the cached keys and values.
Building a KV Cache from Scratch
Below is a minimal example of how one might implement a KV cache in PyTorch. The code is intentionally straightforward to illustrate the core idea without getting bogged down in framework specifics.
import torch
import torch.nn as nn
class SimpleKVCache:
def __init__(self, max_len, d_k, d_v, device='cpu'):
self.max_len = max_len
self.d_k = d_k
self.d_v = d_v
self.device = device
self.keys = torch.zeros((max_len, d_k), device=device)
self.values = torch.zeros((max_len, d_v), device=device)
self.ptr = 0 # pointer to the next free slot
def add(self, key, value):
assert self.ptr < self.max_len, "Cache overflow"
self.keys[self.ptr] = key
self.values[self.ptr] = value
self.ptr += 1
def get(self):
return self.keys[:self.ptr], self.values[:self.ptr]
class TransformerBlock(nn.Module):
def __init__(self, d_model, n_heads, d_k, d_v, device='cpu'):
super().__init__()
self.q_proj = nn.Linear(d_model, d_k * n_heads, bias=False)
self.k_proj = nn.Linear(d_model, d_k * n_heads, bias=False)
self.v_proj = nn.Linear(d_model, d_v * n_heads, bias=False)
self.out_proj = nn.Linear(d_k * n_heads, d_model, bias=False)
self.n_heads = n_heads
self.cache = SimpleKVCache(max_len=1024, d_k=d_k, d_v=d_v, device=device)
def forward(self, x):
# x shape: (seq_len, d_model)
q = self.q_proj(x).view(-1, self.n_heads, self.d_k)
k = self.k_proj(x).view(-1, self.n_heads, self.d_k)
v = self.v_proj(x).view(-1, self.n_heads, self.d_v)
# Add current keys and values to cache
for i in range(x.size(0)):
self.cache.add(k[i], v[i])
cached_k, cached_v = self.cache.get()
# Reshape cached tensors for multi‑head attention
cached_k = cached_k.view(-1, self.n_heads, self.d_k)
cached_v = cached_v.view(-1, self.n_heads, self.d_v)
# Compute attention scores
scores = torch.einsum('bhd,thd->bht', q, cached_k) / (self.d_k ** 0.5)
attn = torch.softmax(scores, dim=-1)
out = torch.einsum('bht,thd->bhd', attn, cached_v)
out = out.reshape(-1, self.n_heads * self.d_k)
return self.out_proj(out)
In this snippet, the SimpleKVCache class manages a fixed‑size buffer that stores keys and values as they are generated. The TransformerBlock demonstrates how the cache is updated and queried during the forward pass. While real‑world implementations use more sophisticated data structures—such as ring buffers or GPU‑resident tensors—to handle variable‑length sequences and batch processing, the core principle remains the same.
Performance Gains in Production
Deploying an LLM to a production environment often involves balancing three competing metrics: latency, throughput, and resource utilization. KV caching directly addresses all three.
Latency: By avoiding redundant computations, the time to generate each subsequent token drops dramatically. In practice, users have reported end‑to‑end latency reductions of 30–50% when a KV cache is enabled, especially for long‑context tasks such as document summarization or code generation.
Throughput: When a single GPU is shared among multiple inference requests, the reduced computational load per token allows the system to handle more concurrent users. This is particularly valuable for SaaS offerings where the cost of GPU time is a critical factor.
Resource Utilization: Memory savings are not just a side effect; they enable larger batch sizes or the deployment of bigger models on the same hardware. In cloud environments, this translates to lower infrastructure costs and a smaller carbon footprint.
Beyond the raw numbers, KV caching also simplifies the engineering of inference pipelines. Frameworks such as Hugging Face’s transformers library expose a past_key_values argument that can be reused across calls, effectively exposing the cache to developers. By integrating this mechanism into a microservice architecture, teams can build scalable, low‑latency LLM services without reinventing the wheel.
Future Directions
While the current KV cache paradigm is highly effective, researchers are already exploring ways to push its limits. One promising avenue is dynamic caching, where the cache size is adjusted on the fly based on the model’s attention patterns. Another line of work investigates compressed caches, using techniques like quantization or low‑rank factorization to further reduce memory overhead.
There is also growing interest in cross‑model caching, where a cache built for one model can be reused or adapted for another. This could enable multi‑model inference on a single device, a scenario that is becoming increasingly common in edge deployments.
Finally, as LLMs evolve toward architectures that reduce or eliminate the need for full‑sequence attention—such as sparse transformers or retrieval‑augmented models—the role of KV caching may shift from a performance optimization to a core architectural component. Understanding how to design caches that work seamlessly with these new paradigms will be a key research challenge in the coming years.
Conclusion
KV caches are more than a clever trick; they are a foundational technology that makes large language models practical for real‑world applications. By storing and reusing the key‑value pairs that drive attention, developers can cut latency, boost throughput, and shrink memory footprints—all without sacrificing model quality. Building a cache from scratch offers a rare glimpse into the inner workings of transformers, fostering a deeper appreciation for the engineering that powers modern AI.
As the field continues to mature, we can expect KV caching to evolve alongside new model architectures, becoming even more efficient and versatile. Whether you’re a researcher, a data scientist, or a product engineer, mastering the art of KV caching will equip you to build the next generation of AI services that are both powerful and production‑ready.
Call to Action
If you’re ready to take your LLM deployments to the next level, start experimenting with KV caching today. Try implementing a simple cache in your favorite deep‑learning framework, measure the impact on latency and throughput, and compare the results against a baseline without caching. Share your findings on GitHub or in the comments below—your insights could help others navigate the same challenges.
For a deeper dive, check out the original article that inspired this post, “Coding the KV Cache in LLMs” by Sebastian Raschka, which provides a step‑by‑step guide and practical code examples. By building on that foundation, you’ll be well‑positioned to innovate in the fast‑moving world of large‑scale language modeling.
