GPU-Validated · NVIDIA H100 SXM

The AI Efficiency
Layer.

An invisible software layer that compresses KV cache load and reduces memory overhead — across text, image, audio, and video. No retraining. No weight changes. Built for production inference.

Explore technology Get Access

456×

Memory Reduction

115 GB → 252 MB on Mistral-7B.

8×

KV Compression

Mistral-7B · +9.35% PPL.

1 LINE

Integration

Drop-in compatible. Works with your stack.

HF + vLLM

Supported Stack

Seamless support for Hugging Face & vLLM.

THE COMPRESSION FIELD

Before Syntropic.
After Syntropic.

115 GB → 252 MB · 456× memory reduction.
100 users at 8K-token prompts, Mistral-7B.

456× MEMORY

8× KV COMPRESSION

NO RETRAINING

VIEW METHODOLOGY →

Operational simplicity

Drop syntropic.activate(model) into an existing transformer workflow and keep inference behavior intact.

Benchmark credibility

Real runs on NVIDIA H100 SXM hardware, with model quality tracked directly instead of inferred from synthetic estimates.

One compression core

A single compression layer extends across text, embeddings, image, audio, and video workloads without introducing a fragmented product surface.

By the Numbers

GPU-Validated
Performance Metrics

How It Works

Three Steps to
456× Memory Reduction

Syntropic patches your model's attention layers transparently. Inference runs exactly as before — compression is invisible.

Step 01 — Install: one pip command, HuggingFace / vLLM / PyTorch supported

Step 03 — Measure: real-time compression ratio, quality, memory saved

Step 02 — Activate: one function call patches all attention layers

456× memory reduction · 8× KV compression (Mistral-7B, +9.35% PPL) · 100 users at 8K-token prompts · No retraining · View methodology

The Solution

One Line.
456× Smaller.

Transparent to your inference pipeline. Your application calls the model exactly the same way — Syntropic handles everything inside the attention layers.

Works with Mistral, Llama, Falcon, GPT-NeoX & all HuggingFace models
CPU + CUDA GPU — same API, zero configuration
Fully reversible with syntropic.deactivate(model)
Real-time per-head, per-layer compression telemetry
vLLM drop-in backend — no code changes required

quickstart.pyCOMPRESSION ACTIVE
# pip install syntropic[huggingface]

from transformers import AutoModelForCausalLM
import syntropic

model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1"
)

# One line — patches all attention layers
syntropic.activate(model)

# Inference is unchanged
outputs = model.generate(**inputs)

# Live stats
stats = syntropic.get_compression_stats(model)
# → {'ratio': 8, 'memory_reduction': 456,
#    'ppl_delta': 0.0935}

syntropic.deactivate(model)  # fully reversible
KV Ratio
8×
Memory
456×
PPL Δ
+9.35%
Status
RUNNING

Core Technology

70+ Patent-Pending Innovations

Five patent layers. Syntropic outperforms Google's TurboQuant (ICLR 2026) by +5.3% cosine fidelity at 1-bit compression — GPU-validated on real hardware.

L1 · Core Algorithm

Layer 1 · ~15 patents

The compression math — quantization, ratio control, fidelity.

L3 · Training-Side Lift

Layer 3 · ~6 patents

Training-time techniques that carry into inference.

70+

Innovations

Five-layer architecture · +L5 Security & Compliance (~8)

L2 · Inference Systems

Layer 2 · ~12 patents

Runtime, KV-cache integration, and serving at scale.

L4 · Multimodal & Domain

Layer 4 · ~10 patents

Text, image, audio, video and domain-specific lifts.

Validation Pipeline

Production Benchmarks.
H100 SXM Validated.

VALIDATED ON: NVIDIA H100 SXM · Production-grade benchmarks · Mistral-7B & Llama-3.1-8B · Real forward passes · v0.8.0 Docker container.

VALIDATION COMPLETE

REAL HARDWARE

Production-grade GPUs

REAL MODELS

Live forward passes

REAL METRICS

Perplexity that matters

REAL VALIDATION

Verified. Reproducible. Trusted.

Benchmark Report

GPU-Validated
Performance Metrics

Forward-pass benchmarks on production hardware. Real models. Real perplexity. No simulations.

KV COMPRESSION

0×

Mistral-7B on H100 SXM

MEMORY REDUCTION

0×

115 GB → 252 MB

PPL DELTA

+0%

384-token context · Mistral-7B

vs TURBOQUANT

+0%

cosine fidelity at 1-bit

KV Cache Memory

Before vs After Syntropic · Mistral-7B · 100 users @ 8K-token prompts

456× LESS

BEFORE

115 GB

AFTER

252 MB

≈115GB freed 252 MBremaining 456×memory

Quality Preservation

PPL delta vs context length · Mistral-7B · 8× KV compression

+9.35% PPL

+9.35%PPL @ 384 tok +7.14%PPL @ 2,048 tok 8×KV compression

VALIDATED ON

NVIDIA H100 SXM

80 GB · Hopper · HBM3

Mistral-7B & Llama-3.1-8B

Real forward passes · production-grade

v0.8.0 Docker container

Reproducible benchmark harness

View runs

View benchmark methodology

Products

One Compression Core.
Ten Future Product Lines.

Syntropic Inference ships today as the v0.8.0 Docker container. The remaining lines are roadmap — sequenced across 2026–2027.

Target Markets

16 Industries.
$300B+ TAM.

Every industry that generates, stores, or transmits AI data. Syntropic's compression works across all of them — text, image, audio, video, embeddings.

Financial Projections

Pure Software.
80%+ Gross Margins.

Four revenue streams: software licensing ($50K–$360K/yr), OEM royalties, Syntropic Cloud API (per-token), and services. Forward-looking estimates.

Revenue Growth

$135MYear 5

Gross Margin

72%Y5 Margin

Clients Growth

200Y5 Clients

Metric	Year 1	Year 2	Year 3	Year 4	Year 5
Revenue	$3.5M	$12M	$32M	$68M	$135M
EBITDA	$1.75M	$7M	$20.5M	$46M	$97M
Margin	50%	58%	64%	68%	72%
Clients	8	25	60	120	200

* Forward-looking estimates. Actual results may differ materially. GPU-validated benchmarks marked separately. Not an offer to sell securities.

Team

Built by the
People Who Invented It.

A focused team turning patent-pending compression research into production infrastructure.

T-01

Josef Elimelech

Founder & Inventor

Creator of all 76+ patent claims across both platforms — inventor of record on every USPTO filing. Technical architect of PODOS Pod, MEGA SILO, Syntropic, and Optimus.

T-02

Greg McNulty

Chief Executive Officer

Former Microsoft executive. Enterprise-scale operational leadership and institutional investor relationships taking PODOS AI from invention to global market.

T-03

Mike Sherman

Chief Technology Officer

Built the Syntropic GPU benchmark suite — validated KV-cache compression on Mistral-7B & Llama-3.1-8B on NVIDIA H100 SXM. Engineering lead for Optimus and Syntropic.

T-04

Barbara Liebeck

VP Sales & Business Dev.

Enterprise account management across AI infrastructure. Leads the customer pipeline for EcoSynQ, the Israel market, and hyperscaler prospects.

T-05

Rafael Smadja

Graphic Designer & Web

Brand identity, thesyntropic.com, and PODOS AI web presence. Translates the technical platform into investor-grade visual communications.

The AI Efficiency Layer.

Before Syntropic. After Syntropic.

GPU-ValidatedPerformance Metrics

Three Steps to456× Memory Reduction

One Line.456× Smaller.

70+ Patent-Pending Innovations

Production Benchmarks.H100 SXM Validated.

GPU-ValidatedPerformance Metrics

One Compression Core.Ten Future Product Lines.

16 Industries.$300B+ TAM.

Pure Software.80%+ Gross Margins.

Built by thePeople Who Invented It.

The AI Efficiency
Layer.

Before Syntropic.
After Syntropic.

GPU-Validated
Performance Metrics

Three Steps to
456× Memory Reduction

One Line.
456× Smaller.

Production Benchmarks.
H100 SXM Validated.

GPU-Validated
Performance Metrics

One Compression Core.
Ten Future Product Lines.

16 Industries.
$300B+ TAM.

Pure Software.
80%+ Gross Margins.

Built by the
People Who Invented It.