🦙

Mpizara Llama.cpp

AI

Môtô famintinana C mahomby ho an'ny modely LLaMA miaraka amin'ny mpizara HTTP

CPU ihany no ampiasaina amin'ny mpizara VPS.org: tsy azo atao ny mametraka ny mpizara GPU. Miasa amin'ny CPU ny Llama.cpp Server, ary manomboka rehefa nametraka raki-tsarin'ny GGUF ao amin'ny mpizara ianao. Mila RAM 2 GB na mihoatra ny sarin'ny modely kely; mila RAM 8 GB eo ho eo ny sarin'ny modely 7B.
Ampiasao amin'ny CPU Jereo ireo rindranasa rehetra

Lazan'ny fampidirana

fanapariahana: 2-5 minitra
sokajy: AI
RAM ambany indrindra: 2048MB
MANAMPY: Tickets sy mailaka

Zarao ity toro-làlana ity

Topy maso

Llama.cpp Server is a high-performance C++ inference engine optimized for running LLaMA and other large language models on commodity hardware. With zero Python dependencies and advanced quantization support (GGUF format), it delivers exceptional performance through CPU-optimized inference, making powerful AI accessible on VPS instances without expensive GPU requirements.

Famaritana fototra

CPU-Optimized Inference

C++ implementation with SIMD acceleration (AVX2, AVX512, NEON) for exceptional CPU performance.

Aggressive Quantization

2-bit to 8-bit quantized models (GGUF) reducing memory footprint while maintaining quality.

OpenAI API Compatibility

HTTP server with /v1/chat/completions, /v1/completions, /v1/embeddings endpoints.

Multi-Architecture Support

Compatible with LLaMA, Mistral, Mixtral, Yi, Phi, Falcon, StarCoder, and more.

Extended Context Windows

Support for 4K to 32K+ tokens with efficient KV cache management.

Production Features

Request queuing, concurrent inference, streaming, Prometheus metrics, health checks.

Fomba fampiasana

- Cost-effective AI API backend replacing OpenAI calls
- Edge and embedded AI deployment on ARM systems
- High-volume batch processing without rate limits
- Privacy-critical applications with on-premise inference
- Real-time AI integration with low-latency streaming
- Offline and air-gapped environments

Toro-làlana fametrahana

Build from source with CMake. Install gcc, g++, cmake, libcurl-dev. Compile with 'make server'. Download GGUF models (Q4_K_M recommended). Create systemd service. Configure Nginx reverse proxy with SSL and rate limiting. Enable huge pages, set CPU governor to performance, bind to specific cores with taskset. Pre-load models with --model-file argument.

Torohevitra momba ny kirakira

Start with --model, --port 8080, --threads, --ctx-size 4096, --batch-size 512. Set --host 0.0.0.0 for network access. Enable metrics with --metrics. Tune --n-gpu-layers, --mlock, --numa, --flash-attn for optimization. Use reverse proxy with authentication. Implement API key validation. Monitor memory with OOM alerts.

Fandraisana an-tànana ara-teknika

Fandraisana an-tànana

  • Fahatsiarovana: 2048RAM 1 MB

Fiankinan-doha

  • ✓ GCC 11+ or Clang 14+
  • ✓ CMake 3.14+
  • ✓ libcurl
  • ✓ GGUF model files

Omeo isa ity lahatsoratra ity

★★★★★
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Maka...

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Vonona ny hametraka ny fampiharanao ve ianao? Mpizara Llama.cpp?

Manomboka ao anatin'ny minitra vitsy amin'ny alalan'ny fizotry ny fametrahana tsotra VPS

Ampiasao amin'ny CPU Jereo ny vidiny

Mandefa amin'ny manodidina ny 2 minitra • Full root access • Fanohanana amin'ny alalan'ny tapakila sy mailaka