ເບິ່ງລວມ
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.
ຄຸນສົມບັດຫຼັກ
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.
ກໍລະນີໃຊ້ງານ
- 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
ຄູ່ມືການຕິດຕັ້ງ
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.
ຂໍ້ແນະນຳການຕັ້ງຄ່າ
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.
ຄວາມຕ້ອງການດ້ານເຕັກນິກ
ສິ່ງທີ່ຂຶ້ນກັບ
- ✓ GCC 11+ or Clang 14+
- ✓ CMake 3.14+
- ✓ libcurl
- ✓ GGUF model files