The Intelligence
of Less.
Extreme Quantization. Recursive Refinement. Deployment Anywhere.
We take 70B-parameter models and compress them to 1B — retaining 99% accuracy. Then we deploy them on your hardware: phones, edge servers, tactical devices. No cloud. No latency. No compromise.
0%
Model Size Reduction
0ms
Average Latency
0x
Cost Reduction
0%
Accuracy Retained
The Bloat vs. The Lean
Why pay for cloud GPU time when you can run intelligence locally?
The Bloat
Server-side LLMs
70B+ parameters
Cloud-hosted GPU clusters
340ms+ latency
Network round-trip included
$2.40 / 1K tokens
Enterprise API pricing
Always connected
Fails without internet
High cost, high latency, zero privacy
The Lean
On-Device Logix SLMs
1B parameters
On-device inference
<23ms latency
Zero network dependency
$0.00 / inference
Local compute, no API
Fully offline
Data never leaves device
Zero cost, sub-ms latency, total sovereignty
From 70B to 1B.
Zero compromise.
Our five-stage pipeline takes massive foundation models and recursively compresses them into edge-deployable artifacts — retaining 99% of the original capability.
Step 1
Ingestion
Raw Model Intake
Ingest any foundation model — Llama, Phi, Gemma, Mistral — along with your proprietary datasets. We profile the architecture and map redundancy.
70B+ params
Source model
Step 2
Distillation
Knowledge Transfer
Teacher-student distillation extracts the critical knowledge pathways. We use the large model as oracle to train a compact student model on your domain.
~8B
Student init
Step 3
Recursive Feedback
Iterative Refinement
The student is evaluated against the teacher across 1000+ domain-specific benchmarks. Weak points are identified and recursively refined over multiple cycles.
47 cycles
Avg. iterations
Step 4
Quantization
Extreme Compression
Apply GPTQ, AWQ, or our proprietary ternary quantization to compress to INT4 or 1.58-bit weights — with near-zero perplexity degradation.
INT4 / 1.58-bit
Target precision
Step 5
Edge Delivery
Deploy Anywhere
Package into optimized ONNX/TFLite runtimes. Deploy to mobile, embedded, on-premise servers, or tactical edge hardware. Zero cloud dependency.
<2GB
Final artifact
Watch the model shrink.
Slide the precision and see real-time impact on model size, latency, and perplexity.
Model
LeanLogix-7B-Medical
Precision
INT4
Model Size
3.8 GB
from 14.2 GB
Compression
73%
size reduction
Latency
75ms
from 340ms
Perplexity
8.32
baseline: 8.2
The lab never sleeps.
Our ML engineering team is actively training, refining, and quantizing models 24/7. Every log entry below is a real snapshot of our recursive refinement pipeline in action.
7
Engineers Online
128
GPUs Active
3
Models Training