TECHNOLOGY

Four modules. One constrained objective.

Reduce memory pressure without hiding correctness, latency, quality, or fallback costs.

MEMORY ORCHESTRATION

Virtual KV Manager

A paged-attention-inspired layer designed to manage KV blocks dynamically and preserve useful context under constrained GPU memory.

Token demandPage mapResidency policyKV block
PURPOSEReduce avoidable KV pressure
STATUSPrototype / integration pending

PREDICTIVE TRANSFER

Entropy-Gated Prefetch

Uses normalized attention entropy to decide when prefetch may be useful while a separate selector decides which pages should move.

Layer 0–2Entropy signalCandidate pagesAsync transfer
PURPOSEHide transfer when useful
STATUSResearch / selective validation

LAYER-AWARE NUMERICS

Precision Folding

Assigns lower precision to shallow layers and higher precision deeper in the model.

Layer depthError budgetPrecision policyKV format
PURPOSESpend precision where it matters
STATUSIntegration pending

LOW-BIT COMPUTE

xQUANT Dequantization

A signed INT4 and MXFP4-style dequantization path intended to reduce bandwidth pressure.

Packed weightsScale metadataDequant kernelTensor compute
PURPOSEReduce weight bandwidth cost
STATUSStandalone kernel passes

CANDIDATE REGISTRY

C0 through C8.

C0

Standard vLLM

Matched baseline control.

C1

FP16 Connector

Measured slower, no physical VRAM win.

C2

INT4 KV

Custom low-bit KV backend.

C3

Hybrid KV

INT2/INT4 research branch.

C4

xQUANT

Weight bandwidth path.

C5

KV + xQUANT

Combined reduction path.

C6

Full Combined Path

Primary target architecture.

C7

FP8 Datacenter

SM90 / H100 branch.

C8

Native MXFP4

SM100 / Blackwell branch.