Model Compression as a Service
Upload your dense .safetensors models. Our secure H100 GPU cluster mathematically restructures the neural pathways, returning a highly-optimized .quantized executable ready for instant edge deployment.
Zero-Retention Policy
Your proprietary weights are processed exclusively in volatile memory (RAM). Upon compilation, the source payload is cryptographically wiped. We retain nothing.
Enterprise Compliance
Our ingestion pipeline operates within SOC2 Type II and HIPAA ready infrastructure. Data in transit is secured via AES-256 TLS 1.3 tunnels.
Guaranteed Uptime SLAs
Enterprise-tier clients receive prioritized H100 queue access with a 99.99% distillation SLA. If a compilation fails, you are never billed for the compute.
Interactive MCaaS Estimator
Estimate the compute cost to compress your proprietary model on our H100 cluster.
Data Ingestion Pipelines
We understand that transferring 20TB of proprietary weights and training data isn't as simple as an HTTP upload. We natively support direct ingestion from your existing cloud data lakes.
Cloud Native Ingestion
- AWS S3 Bucket Sync (IAM Cross-Account)
- Google Cloud Storage (GCS) Transfer
- Azure Blob Storage Hooks
Enterprise Data Lakes
- Databricks Delta Lake Connectors
- Snowflake Secure Data Sharing
- HuggingFace Private Repo Webhooks
Human-in-the-Loop Validation
Compression is destructive if done blindly. We don't just return a binary; we return a certified intelligence report. Our evaluation pipeline ensures your model hasn't lost its core capabilities.
Automated Pre-flight
We run MMLU, HumanEval, and your custom test suites against the uncompressed source model to establish a baseline intelligence score.
Asymmetric Profiling
Our algorithms identify the critical pathways holding the core knowledge, ensuring these nodes are preserved during the aggressive quantization phase.
Impact Report & Sign-off
Before finalizing the `.quantized` binary, we present an impact report comparing the original and compressed scores. You sign off before deployment.
Drag and drop your .safetensors payload here
or click to browse local files. Maximum size: 150GB.
Supported Architectures
The Quantized Labs officially supports the following dense and sparse architectures for automated Phase-State Distillation.
| Model Family | Variants | Distillation Status |
|---|---|---|
| Llama 3 / 3.1 | 8B, 70B | Stable |
| Mistral | v0.1, v0.2, v0.3 | Stable |
| Mixtral MoE | 8x7B, 8x22B | Beta (Memory Spikes) |
| Qwen 2 | All Sizes | Stable |
| Gemma 2 | 2B, 9B, 27B | Stable |
Transparent Pricing
Pay only for the computational time required to compress your model. Once downloaded, you can run the model unlimited times locally for free.
Hobbyist
Perfect for testing the engine with smaller, open-weights models.
- Max Model Size: < 1B Parameters (approx. 2GB)
- Shared GPU Queue
- Community Support
Professional
For startups compressing foundation models for production apps.
- Max Model Size: 40GB
- Priority GPU Queue
- Email Support
Enterprise Fleet
For enterprises compressing proprietary, fine-tuned models at scale.
- Max Model Size: 150GB
- Dedicated H100 Node
- Zero-Retention NDA Guarantee
Frequently Asked Questions
Do I need to pay a licensing fee to use the compiled model?
No. MCaaS is purely a computational service. You pay for the H100 time to compress the model. Once you download the `.quantized` binary, you own it and can distribute it to infinite edge devices with zero royalty fees.
Can you reverse-engineer my model weights?
No. Our ingestion architecture guarantees that weights exist only in volatile memory during the compilation process and are immediately purged.
Why does Mixtral MoE have a memory spike warning?
Sparse Mixture of Experts (MoE) architectures require loading specific experts into RAM dynamically. While our binary size is small, the active memory footprint during inference can spike as different experts are invoked. We recommend dense architectures for strict 2GB RAM limits.