Attention Memory Architecture
Pure Edge Computation
KV Cache Architecture & Latent Compression Sizer
Models KV cache memory savings across context windows up to 128k tokens for MHA, GQA, and MLA architectures.
Formula:
MHA = 2 * H_q * D_h * Seq * P; GQA = 2 * H_kv * D_h * Seq * P; MLA = (D_c + D_pe) * Seq * P; Ratio = MHA / MLA
Model Parameters
Outputs & Underwriting Summary
Calculation Status
COMPRESSED LATENT ATTENTION CACHE
Verified Model
DeepSeek Latent Attention (MLA)
32.0 MB / stream
MHA Baseline: 2,048.0 MB / stream | GQA: 128.0 MB / stream
Attention KV Compression
64.0x reduction
Batch VRAM: 0.25 GB
For sequence length 32,768, standard MHA allocates 2,048.0 MB per sequence. GQA reduces this to 128.0 MB, while DeepSeek MLA compresses it to 32.0 MB (64.0x compression factor relative to MHA).
AutoPublish AI Computational & Analytical Suite
AI Inference Architecture
LLM Reasoning & Inference Cost Calculator
Evaluates hidden chain-of-thought token costs and monthly budget scaling for reasoning models versus standard large language models.
Distributed ML Serving
Transformer KV Cache VRAM Allocation Modeler
Models High-Bandwidth Memory (HBM) consumption for transformer attention layers during long-context serving.
SaaS Operations & Pricing
B2B SaaS Net Revenue Retention (NRR) & Expansion Modeler
Decomposes enterprise customer expansion velocity, down-sell contraction, and logo churn into actionable valuation metrics.