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Cluaiz

CLUAIZ TECHNOLOGY

Hardware-agnostic 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 Database Engine and compute node that runs the models. We support 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 Industrial Alpha (Research Phase). While the core orchestration architecture is stable, hardware-constrained guarantees and ternary kernels are still undergoing rigorous validation.
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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 execution remains unaffected. The internal runtime executes via Native FFI and Shared-Memory. The API we provide is strictly an optional external compatibility layer for ecosystem tools. The actual inference and processing 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-Dependency Native Execution: Cluaiz runs as a single Rust-native binary. You can deploy it directly on any cloud VPS (Google Cloud, AWS, RunPod, or a standard Linux server) with zero external 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 FFI layer and exposes a scalable, non-blocking OpenAI-compatible API server to handle concurrent requests.
  • What about Mobile Ecosystems? While our core engine natively targets Android and mobile architectures for direct-on-device silicon execution, our immediate launch focuses strictly on stabilizing desktop and server infrastructure. Once the core kernel achieves absolute stability, the native mobile runtime will be exposed.
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The VRAM Arbiter is a unique hardware-governance feature. It automatically probes your physical hardware and enforces a strict 7.5% memory safety margin. By mathematically evaluating available physical memory before every single token generation step, it actively prevents Out-of-Memory (OOM) crashes across any hardware scale—from 2GB CPUs to 80GB GPUs.
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Cluaiz optimizes 1-bit and 1.58-bit quantization through dynamic compile-time interception. During the build process, it dynamically patches underlying inference libraries to process models using actual 1-bit calculations (pure addition and subtraction on hardware registers). This completely avoids floating-point conversions and unlocks massive throughput on both CPUs and GPUs.
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Cluaiz supports extending the AI's capabilities through WebAssembly (WASM) Skills. These are secure binaries executing within a strict 64KB isolated ring, giving autonomous agents native system capabilities with zero REST API latency, all while keeping code execution completely sandboxed and secure from the host machine.

Still have questions?

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

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