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Cluaiz

CLUAIZ TECHNOLOGY

Silicon-native runtime infrastructure for high-performance local AI inference on AMD, Intel, Apple Silicon, ARM, and NVIDIA hardware.

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Knowledge Base

Frequently Asked Questions

Everything you need to know about the Cluaiz ecosystem, local inference, and native hardware deployment.

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Cluaiz is built for developers who demand absolute performance and privacy. Here are the core advantages:

  • Native Execution Speed: Bypasses heavy middleware (Docker, Python, Node) to interface directly with your hardware.
  • Low-Latency Execution: Utilizes Shared-Memory Signaling and Native FFI for sub-microsecond latency.
  • Hardware Agnostic: Runs natively on NVIDIA (CUDA), Apple (Metal), AMD/Intel, and ARM (Raspberry Pi).
  • 100% Local & Private: Fully local execution ensures your data never leaves your machine.
  • Future-Proof: Optimized for GGUF today, and ready for next-gen architectures.
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No. Cluaiz is the engine/infrastructure that runs the models. Think of it like an operating system for AI. We support state-of-the-art architectures like Transformers, Mixture-of-Experts (MoE), and BitNet b1.58 ternary models.
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Cluaiz extracts maximum performance from almost any silicon. We support NVIDIA (CUDA Tensor Cores), Apple Silicon (Metal/MPS), AMD/Intel (Vulkan/ROCm), and even ARM processors like Raspberry Pi.
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Cluaiz is released under the Apache License 2.0. It is completely free for personal use, individuals, startups, and enterprise builders to maintain a decentralized, open-source technology framework.
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Unlike traditional wrappers that communicate via HTTP or heavy API layers, Cluaiz uses Shared-Memory Signaling and Native FFI (Foreign Function Interfaces) to talk directly to the inference kernels. This cuts out all middleman overhead.
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Cluaiz is currently in its Alpha phase of development. While it is highly capable and you can use it effectively right now, you might encounter occasional bugs as we rapidly innovate. We are constantly pushing updates to achieve full stability soon.
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Yes, absolutely! You can run any standard GGUF file by simply providing its Hugging Face URL. However, because the engine is still in active development, we highly recommend using the officially supported models from our Registry first for the most stable and optimized experience.
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Currently, Cluaiz exclusively provides full, native support for the GGUF format. Formats like AWQ, GPTQ, and others are not supported at this time. In the future, we will add support for AWQ (featuring concurrent 4-bit and 8-bit architecture support). However, GPTQ will not be supported.
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Yes, Cluaiz includes a built-in API. Currently, it offers basic compatibility so you can already connect with external chatbots and ecosystem tools. It is a work-in-progress, but full, comprehensive API support is coming very soon.
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No, the core engine speed remains completely unaffected. The internal runtime always executes via ultra-fast Native FFI and Shared-Memory. The API we provide is strictly an optional external compatibility layer for ecosystem tools. The actual inference and processing still happen at native hardware speeds.
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Cluaiz is a unified engine designed to scale seamlessly from an 8GB laptop to multi-GPU enterprise servers. It is NOT restricted to local desktops. Here is how Cluaiz architecture handles server and cloud deployments:

  • Zero-Docker Native Execution: Unlike traditional serving frameworks that force you to pull multi-gigabyte Docker containers and bloated Python runtime images, Cluaiz runs as a single, highly-optimized native binary. You can deploy it directly on any cloud VPS (Google Cloud, AWS, RunPod, or a standard Linux server) with zero runtime dependencies.
  • Pure C/C++ & Rust Daemon: You can launch the Cluaiz engine directly from the CLI or run it as a background system service (systemd daemon). Once started via a simple terminal command, it immediately spins up the ultra-fast FFI layer and exposes a highly scalable, non-blocking OpenAI-compatible API server to handle concurrent production requests.
  • What about Mobile Ecosystems? While our core engine natively targets Android and mobile architectures for direct-on-device silicon execution, our immediate production launch focuses strictly on stabilizing desktop and server infrastructure. Once the core kernel achieves absolute stability, the native mobile runtime will be exposed to deliver the same bare-metal inference speeds directly inside mobile applications.

Still have questions?

Join our community or check out the official documentation for deep dives into the architecture.

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