KV Cache Overhead & Multi-User Concurrency Profiler [2026 Formula]

KV Cache Memory Architecture & Scaling Dynamics

As local LLM deployments scale from basic conversational prompts to 32K, 64K, and 128K document retrieval (RAG) workflows, the Key-Value (KV) cache frequently outgrows the model weights themselves.

1. Exact KV Cache Memory Equation

The physical memory occupied by the attention cache is calculated via:

KV_Cache (Bytes) = 2 * n_layers * n_kv_heads * d_head * context_length * precision_bytes * concurrency

Where:

• 2: Represents two distinct tensors per token: the Key vector and the Value vector.
• n_layers: Total transformer decoder layers (e.g., 32 layers for 8B, 64 layers for 32B, 80 layers for 70B).
• n_kv_heads: Number of key/value attention heads (8 heads under Grouped-Query Attention).
• d_head: Dimension of each attention head (typically 128 bytes).
• context_length: The active token depth (e.g., 32,768 tokens).
• precision_bytes: Floating point precision (2 bytes for FP16, 1 byte for FP8, 0.5 bytes for INT4).
• concurrency: Number of parallel client streams simultaneously maintaining active context.

2. Memory Bandwidth Constraints on Token Throughput

During the autoregressive decode phase, generation speed is strictly memory bandwidth bound. For every single token generated, the model weights and the entire KV cache must be read from unified memory:

Required_Bandwidth (GB/s) = (Weights_Size_GB + (KV_Cache_GB / Concurrency)) * Target_Tokens_Per_Second

This explains why standard DDR5 dual-channel setups (60 GB/s) hit a performance wall at 10-14 tokens/second on 32B models, while 256 GB/s Strix Halo architectures maintain 30+ tokens/second under continuous multi-turn dialogue.