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MAGIC

Model hosting and inference · by Pipeshift

Proprietary framework (Modular Architecture for GPU Inference Clusters) that compiles workload-specific inference pipelines, controlling layers from model to silicon including KV caching, custom kernels, speculative engine, model parallelism, quantization, and GPU orchestration.

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What it does

Proprietary framework (Modular Architecture for GPU Inference Clusters) that compiles workload-specific inference pipelines, controlling layers from model to silicon including KV caching, custom kernels, speculative engine, model parallelism, quantization, and GPU orchestration.

  • Deploy and serve open-source, custom, and fine-tuned AI models in production
  • Scale real-time AI agents across clouds and regions with ultra-low latency
  • Orchestrate multi-region AI inference workloads with automated failover and routing
Category
Model hosting and inference
Built for
Real estate and construction
Users
Teams deploying large language models and compound AI systems, Organizations requiring multi-region AI inference reliability, Builders of real-time AI agents including voice, coding, and chat applications
Public API
Not offered

Not yet disclosed: deployment options, platforms, languages and support channels.

Pricing

  • CustomCustom
    • Quoted by their sales team

Prices as published by Pipeshift, checked Oct 2026.

Security and compliance

As reported by Pipeshift. Ask for audit reports before you buy.

  • end-to-end-data-encryption
  • rbac
  • penetration-testing

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