AI Architecture Efficiency Framework

Interactive Efficiency Calculator

Input any model's architectural parameters and target hardware specs. The framework computes all five KPIs and renders the Roofline position in real time.

Model Architecture

d_model (hidden size)

KV latent dim (d_kv)

Q latent dim (d_q)

Attention heads

Head dim

Layers

Active experts

Expert FFN dim

Baseline bits (FP16=16)

Quantized bits (FP4=4)

Hardware Target

Peak FLOPS (TFLOPS, FP8)

Memory BW (GB/s)

VRAM (GB)

GPU cost ($)

Context length (tokens)

# GPUs in cluster

—

R_comp

Compression Ratio

—

ΔOI

Roofline Shift

—

G_bits

Bit-Width Leverage

—

Q/KV Ratio

Entropy Asymmetry

—

Max Batch

@Target Context

—

HW Leverage

Virtual $ per Real $

Diagnostic Summary

Adjust parameters above to generate analysis.

Roofline Position Map

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This framework was produced by InAI Capital Advisor as part of our ongoing coverage of the global AI investment landscape. The analysis represents proprietary research conducted through expert network consultations and primary technical evaluation.

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