Tamp your models.
Run them anywhere.
We reduce AI models to run fast on CPUs and on-device, and deploy privately in your environment.
Building with teams who care about cost, latency, and private AI
Engineered for efficiency.
Our core technique enables high-performance inference on restricted hardware.
CPU-first performance
Target real CPU bottlenecks, not just smaller weights. Run LLMs on commodity hardware.
Intelligent Optimization
Automatically identify and optimize redundant computations without retraining from scratch.
Pairs with quantization
Stack architecture-aware compression with standard pruning and quantization for max gains.
Quality-aware
Evaluation harness and regression checks per task to ensure model fidelity.
Deploy anywhere
Run on commodity CPU fleets, edge devices, and privacy-sensitive on-prem environments.
Developer tooling
SDK/CLI + detailed reports showing speed/memory/quality tradeoffs.
How it works
Profile
Identify bottlenecks in your model architecture.
Compress
Proprietary reduction removes redundant compute while maintaining quality.
Deliver
Ship as a reduced artifact or serve via private runtime/API.
OUTPUT EXAMPLE
Visual Fidelity, Compressed.
Experience the same quality with significantly lower VRAM usage. Comparing original Wan 2.1 face swaps against our optimized versions.
1Inputs
2Results: VRAM Efficiency
Version 1: Original
Version 2: Tamped
-36% VRAMVersion 3: Tamped
-42% VRAMReal impact on inference.
We drastically reduce the computational cost of running large models, making them viable for production on standard hardware.
* Results vary by model/task. Report provided per run.
Benchmark: Llama-3-8B (CPU)
Use Cases
High-potential deployment wedges.
The strongest opportunities combine immediate cost pressure, large inference volumes, and repeatable expansion into broader model portfolios.
AI API Platforms
Lower per-token inference cost for high-volume chat, agent, and assistant traffic.
GPU Cloud Providers
Offer a CPU-optimized inference tier to increase margin and reduce GPU bottlenecks.
On-device Copilots
Run capable local models on laptops and mobile hardware with lower latency and stronger privacy.
Regulated Enterprise AI
Enable on-prem and private-cloud inference where data residency and compliance are mandatory.
Industrial Edge Automation
Deploy compact models for robotics and real-time decision loops in constrained environments.
Media and Video Pipelines
Scale content analysis and generation workloads with higher throughput at lower compute cost.
Make GPU-class models
CPU-friendly.
Send a model + target hardware. We’ll return a reduced artifact and a performance report.