KV-Cache

During text generation, a transformer model produces one token at a time. Without a KV-Cache, the model would recompute the Key and Value projections for all previous tokens at each step, resulting in quadratic computational complexity. The KV-Cache stores these tensors from prior steps, so each new token generation only computes attention for the newest token against the stored history. This reduces the computational cost of each decoding step from O(n²) to O(n), where n is the sequence length.

The KV-Cache consumes significant GPU memory, growing linearly with batch size, sequence length, and number of layers. For a model with L layers, h attention heads, and dimension d, the cache size per token is 2 * L * h * d. For long sequences or large batches, this can exceed available VRAM, becoming the primary bottleneck. Techniques like Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) reduce the number of Key and Value heads to shrink the cache footprint.

In serving systems, the KV-Cache for a shared system prompt or common prefix can be computed once and reused across multiple distinct requests. This prefix caching avoids redundant prefill computation. When a new request shares a prefix with a previously processed request, the scheduler can fork the existing KV-Cache, dramatically reducing time-to-first-token (TTFT) and total compute cost for applications like chatbots with long system instructions.

Traditional KV-Cache allocation reserves a contiguous block of memory for the maximum possible sequence length, leading to severe internal fragmentation and memory waste. PagedAttention, introduced in vLLM, manages the KV-Cache in fixed-size blocks (pages) that can be mapped non-contiguously, similar to virtual memory in operating systems. This eliminates fragmentation, allows dynamic memory sharing, and enables near-optimal GPU memory utilization with 2-4x higher throughput.

To mitigate the memory burden, the stored Key and Value tensors can be quantized to lower precision:

• KV8/FP8: Storing K and V in 8-bit floating point instead of FP16, halving memory usage with minimal accuracy loss.
• KV4: Aggressive 4-bit quantization for extreme compression.
• Token eviction: Heuristics like StreamingLLM drop the KV entries for middle tokens while preserving initial tokens (attention sinks) and recent tokens, enabling theoretically infinite sequence lengths.

The KV-Cache lifecycle has two distinct phases:

• Prefill: The entire input prompt is processed in parallel. All Key and Value tensors are computed and stored in the cache. This phase is compute-bound.
• Decode: Tokens are generated one at a time. Each step appends a single new K/V entry to the cache and attends over the full history. This phase is memory-bound, with the bottleneck being the transfer of the KV-Cache from HBM to the compute units.

An optimization that reuses the computed KV-Cache for a shared prompt prefix across multiple distinct generation requests. When many users query a system with the same lengthy system prompt, prefix caching stores the Key and Value tensors for that common prefix once, eliminating redundant computation for every subsequent request. This dramatically reduces time-to-first-token (TTFT) and GPU compute costs in high-traffic sovereign deployments.

An inference acceleration technique that leverages the KV-Cache to validate multiple tokens in parallel. A small, fast draft model proposes several future tokens autoregressively, while a larger verifier model processes them in a single forward pass using the draft's KV-Cache. If the verifier accepts the tokens, generation leaps forward; if not, the verifier's KV-Cache is used to correct the sequence. This maintains the exact output distribution of the target model while reducing latency.

The deterministic algorithm that decides which KV-Cache entries to remove when GPU memory reaches capacity. Common strategies include:

• Least Recently Used (LRU): Discards the KV-Cache with the oldest access timestamp
• First-In-First-Out (FIFO): Evicts based on insertion order
• Time-To-Live (TTL): Invalidates entries after a fixed duration Choosing the right policy balances memory utilization against cache hit rate in sovereign inference clusters.

A multi-level storage strategy for KV-Cache data that places the hottest, most frequently accessed tensors in GPU VRAM while offloading cooler, less frequently accessed caches to CPU RAM or even NVMe storage. This hierarchical approach allows sovereign deployments to maintain massive effective cache sizes without requiring prohibitively expensive GPU memory, swapping KV-Cache blocks between tiers based on access patterns.

A horizontally scalable architecture that spreads KV-Cache storage across multiple nodes or GPU clusters. Using consistent hashing, the system deterministically maps specific cache keys to specific nodes, minimizing reshuffling when nodes join or leave. This enables sovereign deployments to serve high-throughput inference with low-latency access to shared KV-Cache state, essential for elastic scaling in air-gapped environments.

The automated collection of metrics from the KV-Cache subsystem to provide observability into performance. Critical metrics include:

• Cache hit ratio: Percentage of tokens served from cache vs. recomputed
• Memory pressure: GPU VRAM consumed by stored KV-Cache blocks
• Eviction rate: Frequency of cache entry removal This telemetry is essential for tuning cache size and eviction policies in production sovereign inference pipelines.