KV Cache Optimization: Memory Efficiency for Production LLMs

December 2025 Update: Traditional inference wasting 60-80% of KV cache memory through fragmentation. vLLM's PagedAttention reducing waste to under 4%, enabling 2-4x throughput. 70B model with 8K context requiring ~20GB cache per request, ~640GB for batch of 32. KV cache now often exceeds model weights in memory consumption. Optimization techniques enabling longer contexts and larger batches on existing hardware.

LLM inference systems waste 60-80% of allocated KV cache memory through fragmentation and over-allocation.¹ That waste translates directly into reduced throughput, higher costs, and artificial limits on context lengths. PagedAttention, introduced by vLLM, slashed KV cache waste to under 4%, enabling 2-4x throughput improvements that transformed production inference economics.² Understanding KV cache optimization techniques helps organizations maximize GPU utilization and serve more users from existing infrastructure.

KV cache management has become the critical bottleneck for production LLM deployments. Memory consumption grows linearly with sequence length and batch size, quickly exhausting even high-memory GPUs like H100 and H200. Mastering cache optimization techniques enables longer contexts, larger batches, and more cost-effective inference at scale.

Why KV caching matters

Transformer models compute attention over all previous tokens when generating each new token. Without caching, generating 1,000 tokens requires recomputing attention from scratch 1,000 times—quadratically increasing cost with sequence length.

KV caching solution: Store key and value tensors from previous tokens, reusing them for subsequent attention calculations. Each new token computes attention against cached values rather than regenerating them.

Memory impact: A 70B parameter model generating 8,192 tokens with batch size 32 requires approximately 40-50GB of KV cache memory alone—often exceeding the model weights themselves.³

The scaling problem: KV cache memory grows as:

Memory = batch_size × seq_length × num_layers × 2 × hidden_dim × precision_bytes

For Llama 3.1-70B with FP16: - Per-token cache: ~2.5MB - 8K context: ~20GB per request - Batch of 32: ~640GB total KV cache

PagedAttention: the foundational optimization

vLLM's PagedAttention revolutionized KV cache management by treating GPU memory like operating system virtual memory:⁴

How it works

Traditional allocation: Reserve contiguous memory blocks for maximum possible sequence length. A 4K max context allocates 4K worth of cache even for 100-token requests, wasting 97.5% of reserved memory.

Paged allocation: Divide KV cache into fixed-size blocks (pages). Allocate pages on-demand as sequences grow. Free pages when sequences complete.

Block table mapping: Like OS page tables, PagedAttention maintains mappings between logical sequence positions and physical memory locations. Sequences see continuous memory while physical storage remains non-contiguous.

Performance impact

• Memory waste: 60-80% → under 4%
• Throughput: 2-4x improvement versus traditional allocation
• Memory fragmentation: Virtually eliminated⁵

Implementation in vLLM

from vllm import LLM, SamplingParams

# PagedAttention enabled by default
llm = LLM(
    model="meta-llama/Llama-3.1-70B-Instruct",
    tensor_parallel_size=4,
    gpu_memory_utilization=0.90,  # Use 90% of GPU memory
    max_model_len=32768,
)

vLLM automatically manages page allocation, deallocation, and memory sharing without explicit configuration.

Prefix caching and memory sharing

PagedAttention enables efficient memory sharing across requests with common prefixes:

Shared system prompts: When multiple requests use identical system prompts, physical pages storing those tokens get shared rather than duplicated.

Automatic prefix caching: vLLM's Automatic Prefix Caching (APC) detects common prefixes across requests and shares KV cache blocks automatically:

llm = LLM(
    model="meta-llama/Llama-3.1-8B-Instruct",
    enable_prefix_caching=True,
)

Production impact: Applications with consistent system prompts or repeated context (RAG with common documents, few-shot examples) see dramatic memory savings and latency reduction. Cache hit rates of 87%+ are achievable with well-structured prompts.⁶

KV cache quantization

Compressing KV cache values reduces memory requirements at the cost of minor accuracy degradation:

FP8 KV cache

Hopper and Blackwell GPUs support native FP8 KV cache:

# vLLM FP8 KV cache
llm = LLM(
    model="meta-llama/Llama-3.1-70B-Instruct",
    kv_cache_dtype="fp8",
)

FP8 halves KV cache memory versus FP16 with minimal quality impact for most applications. The optimization becomes essential for long-context inference where cache dominates memory consumption.

INT4 KV cache

Experimental 4-bit KV cache support further reduces memory:⁷ - Memory reduction: 4x versus FP16 - Quality impact: Task-dependent, requires evaluation - Best for: Memory-constrained long-context applications

Quantization selection

Cache eviction strategies

When memory pressure exceeds available capacity, cache eviction policies determine which tokens to drop:

Sliding window attention

Maintain only recent tokens in cache, dropping older context:

# Conceptual sliding window
def sliding_window_cache(kv_cache, window_size):
    if len(kv_cache) > window_size:
        kv_cache = kv_cache[-window_size:]
    return kv_cache

Simple but effective for applications where recent context matters most. Architectural sliding window (like Mistral) implements this natively.

Attention-based eviction

Remove tokens with lowest attention scores, keeping important context:

PagedEviction (2025): Block-wise eviction algorithm tailored for PagedAttention that identifies and removes low-importance blocks without modifying CUDA kernels.⁸

Entropy-guided caching: Allocate cache budget based on layer attention entropy—layers with broader attention patterns receive more cache, focused layers receive less.⁹

Streaming LLM

For infinite-length generation, Streaming LLM maintains: - Initial "attention sink" tokens (first 4-8 tokens) - Recent tokens within sliding window - Drops middle context

The approach enables theoretically unlimited generation with fixed memory, though quality degrades for tasks requiring long-range dependencies.

KV cache offloading

When GPU memory proves insufficient, offload cache to slower storage tiers:

CPU offloading

Move inactive sequence caches to system RAM:

# LMCache integration for offloading
from lmcache import LMCacheEngine

cache_engine = LMCacheEngine(
    backend="cpu",
    max_gpu_cache_size="20GB",
    cpu_cache_size="100GB",
)

Latency impact: CPU-GPU transfer adds 10-50ms per cache retrieval. Suitable for batch workloads or when GPU memory limits prevent serving at all.

Performance: LMCache with vLLM achieves 3-10x latency reduction versus recomputation by caching in CPU memory rather than regenerating.¹⁰

Disk offloading

For extreme cases, cache to NVMe storage: - Latency: 100-500ms per retrieval - Use case: Very long contexts that would otherwise be impossible - Not suitable for interactive applications

Tiered caching

Production systems often implement multi-tier caching:

1. GPU HBM: Hot sequences actively generating
2. CPU RAM: Warm sequences recently active
3. NVMe SSD: Cold sequences for potential reuse

Intelligent promotion and demotion policies move cache between tiers based on access patterns.

KV cache-aware routing

Distributed inference benefits from routing requests to pods holding relevant cache:

llm-d framework

Kubernetes-native framework with KV cache-aware routing:¹¹

# llm-d cache routing configuration
routing:
  strategy: kv_cache_aware
  cache_hit_weight: 0.8
  load_balance_weight: 0.2

Performance results: - 87% cache hit rate with prefix-heavy workloads - 88% faster time-to-first-token for warm cache hits - Significant reduction in redundant computation across cluster

Implementation patterns

Sticky sessions: Route requests from same conversation to same pod.

Prefix hashing: Hash system prompts to determine pod routing, ensuring prefix cache hits.

Load-aware routing: Balance cache locality against pod utilization to prevent hotspots.

Production sizing guide

Memory estimation

Calculate KV cache requirements before deployment:

def estimate_kv_cache_memory(
    num_layers: int,
    hidden_dim: int,
    num_kv_heads: int,
    head_dim: int,
    max_seq_len: int,
    max_batch_size: int,
    precision_bytes: int = 2,  # FP16
) -> float:
    """Estimate KV cache memory in GB"""
    per_token = num_layers * 2 * num_kv_heads * head_dim * precision_bytes
    total = per_token * max_seq_len * max_batch_size
    return total / (1024 ** 3)

# Llama 3.1-70B example
memory_gb = estimate_kv_cache_memory(
    num_layers=80,
    hidden_dim=8192,
    num_kv_heads=8,  # GQA
    head_dim=128,
    max_seq_len=8192,
    max_batch_size=32,
)
print(f"KV cache memory: {memory_gb:.1f} GB")

Capacity planning

Rule of thumb: Reserve 40-60% of GPU memory for KV cache, remainder for model weights and activations.

Example H100 80GB: - Model weights (70B FP16): ~140GB → 2x GPU with tensor parallelism - Per-GPU available for cache: ~30-35GB after weights and overhead - Maximum concurrent sequences: Depends on average context length

Optimization priority

1. Enable PagedAttention: Default in vLLM, major efficiency gain
2. Enable prefix caching: If workloads have common prefixes
3. Implement FP8 KV cache: When using Hopper/Blackwell GPUs
4. Add cache-aware routing: At cluster scale with distributed inference
5. Consider offloading: Only when GPU memory proves insufficient

Monitoring and observability

Track KV cache metrics in production:

Key metrics: - Cache utilization: Percentage of allocated cache in use - Cache hit rate: Prefix cache effectiveness - Eviction rate: Frequency of cache overflow - Memory fragmentation: Wasted space within allocated blocks

vLLM metrics endpoint:

# Prometheus metrics available at /metrics
# kv_cache_usage_percent
# kv_cache_total_blocks
# kv_cache_used_blocks
# prefix_cache_hit_rate

Alerting thresholds: - Cache utilization > 90%: Scale capacity or reduce batch size - Hit rate < 50%: Review prefix caching configuration - Eviction rate high: Increase memory allocation or optimize prompts

Organizations deploying production LLM inference can leverage Introl's infrastructure expertise for GPU capacity planning and optimization across global deployments.

The memory efficiency imperative

KV cache optimization represents one of the highest-impact improvements for production LLM deployments. PagedAttention alone delivers 2-4x throughput improvements—equivalent to doubling or quadrupling GPU investment without additional hardware cost.

The optimization landscape continues evolving. Microsoft's FastGen demonstrated 50% memory reduction through adaptive compression. Entropy-guided caching allocates budget intelligently across layers. Cache-aware routing enables cluster-scale efficiency gains previously impossible.

For organizations running inference at scale, KV cache optimization should rank among the first optimizations evaluated. The techniques require minimal code changes while delivering substantial cost and performance improvements. Every percentage of cache efficiency gained translates directly into more requests served, longer contexts supported, or infrastructure costs reduced. In the economics of production LLM inference, mastering KV cache management separates efficient deployments from expensive ones.

References

1. vLLM. "Efficient Memory Management for Large Language Model Serving with PagedAttention." ACM SOSP 2023. https://dl.acm.org/doi/10.1145/3600006.3613165

2. ———. "Efficient Memory Management for Large Language Model Serving with PagedAttention."

3. Sebastian Raschka. "Understanding and Coding the KV Cache in LLMs from Scratch." Magazine. 2024. https://magazine.sebastianraschka.com/p/coding-the-kv-cache-in-llms

4. vLLM Documentation. "Paged Attention." Accessed December 8, 2025. https://docs.vllm.ai/en/latest/design/paged_attention.html

5. Medium. "The Architecture Behind vLLM: How PagedAttention Improves Memory Utilization." By Mandeep Singh. 2024. https://medium.com/@mandeep0405/the-architecture-behind-vllm-how-pagedattention-improves-memory-utilization-2f9b25272110

6. Red Hat Developer. "Master KV cache aware routing with llm-d for efficient AI inference." October 7, 2025. https://developers.redhat.com/articles/2025/10/07/master-kv-cache-aware-routing-llm-d-efficient-ai-inference

7. GitHub. "Support for 4-bit KV Cache in paged-attention op." vLLM Issues #4025. https://github.com/vllm-project/vllm/issues/4025

8. arxiv. "PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference." 2025. https://arxiv.org/html/2509.04377v1

9. MDPI Mathematics. "Entropy-Guided KV Caching for Efficient LLM Inference." July 2025. https://www.mdpi.com/2227-7390/13/15/2366

10. BentoML. "KV cache offloading." LLM Inference Handbook. 2025. https://bentoml.com/llm/inference-optimization/kv-cache-offloading

11. Red Hat Developer. "Master KV cache aware routing with llm-d for efficient AI inference."