[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71PYSAQVG1DFV10RH0P8NTS":3,"skill-same:01M71PYSAQVG1DFV10RH0P8NTS":88},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":13,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M71PYSAQVG1DFV10RH0P8NTS","llama-cpp",null,"Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.","gjbvsjjvgm-sketch\u002FWORM_V2_SOVEREIGN","gjbvsjjvgm-sketch","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F278511911?v=4","01M71F3JXKZQ70H2KCHHTXKZDA","01M71PENEVFN19DBZFNHJPCHQ7",0,"MIT","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002FWORM_V2_SOVEREIGN\u002Fblob\u002Fmain\u002Fcore\u002Fhermes_sovereign_data\u002Fskills\u002Fmlops\u002Finference\u002Fllama-cpp\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"llama cpp","Llama","cpp","orchestra","NVIDIA","Intel GPUs","AMD GPUs","Non-NVIDIA","Edge Deployment","CPU Inference","llama.cpp","GGUF","silicon","CPU","Inference Serving","apple","when","pytorch","memory","bit","2026-11-10 13:02:02",false,"---\nname: llama-cpp\ndescription: Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [llama-cpp-python]\nmetadata:\n  hermes:\n    tags: [Inference Serving, Llama.cpp, CPU Inference, Apple Silicon, Edge Deployment, GGUF, Quantization, Non-NVIDIA, AMD GPUs, Intel GPUs, Embedded]\n\n---\n\n# llama.cpp\n\nPure C\u002FC++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.\n\n## When to use llama.cpp\n\n**Use llama.cpp when:**\n- Running on CPU-only machines\n- Deploying on Apple Silicon (M1\u002FM2\u002FM3\u002FM4)\n- Using AMD or Intel GPUs (no CUDA)\n- Edge deployment (Raspberry Pi, embedded systems)\n- Need simple deployment without Docker\u002FPython\n\n**Use TensorRT-LLM instead when:**\n- Have NVIDIA GPUs (A100\u002FH100)\n- Need maximum throughput (100K+ tok\u002Fs)\n- Running in datacenter with CUDA\n\n**Use vLLM instead when:**\n- Have NVIDIA GPUs\n- Need Python-first API\n- Want PagedAttention\n\n## Quick start\n\n### Installation\n\n```bash\n# macOS\u002FLinux\nbrew install llama.cpp\n\n# Or build from source\ngit clone https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\ncd llama.cpp\nmake\n\n# With Metal (Apple Silicon)\nmake LLAMA_METAL=1\n\n# With CUDA (NVIDIA)\nmake LLAMA_CUDA=1\n\n# With ROCm (AMD)\nmake LLAMA_HIP=1\n```\n\n### Download model\n\n```bash\n# Download from HuggingFace (GGUF format)\nhuggingface-cli download \\\n    TheBloke\u002FLlama-2-7B-Chat-GGUF \\\n    llama-2-7b-chat.Q4_K_M.gguf \\\n    --local-dir models\u002F\n\n# Or convert from HuggingFace\npython convert_hf_to_gguf.py models\u002Fllama-2-7b-chat\u002F\n```\n\n### Run inference\n\n```bash\n# Simple chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    -p \"Explain quantum computing\" \\\n    -n 256  # Max tokens\n\n# Interactive chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --interactive\n```\n\n### Server mode\n\n```bash\n# Start OpenAI-compatible server\n.\u002Fllama-server \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 32  # Offload 32 layers to GPU\n\n# Client request\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"llama-2-7b-chat\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Quantization formats\n\n### GGUF format overview\n\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\n|--------|------|-----------|-------|---------|----------|\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\n\n### Choosing quantization\n\n```bash\n# General use (balanced)\nQ4_K_M  # 4-bit, medium quality\n\n# Maximum speed (more degradation)\nQ2_K or Q3_K_M\n\n# Maximum quality (slower)\nQ6_K or Q8_0\n\n# Very large models (70B, 405B)\nQ3_K_M or Q4_K_S  # Lower bits to fit in memory\n```\n\n## Hardware acceleration\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake LLAMA_METAL=1\n\n# Run with GPU acceleration (automatic)\n.\u002Fllama-cli -m model.gguf -ngl 999  # Offload all layers\n\n# Performance: M3 Max 40-60 tokens\u002Fsec (Llama 2-7B Q4_K_M)\n```\n\n### NVIDIA GPUs (CUDA)\n\n```bash\n# Build with CUDA\nmake LLAMA_CUDA=1\n\n# Offload layers to GPU\n.\u002Fllama-cli -m model.gguf -ngl 35  # Offload 35\u002F40 layers\n\n# Hybrid CPU+GPU for large models\n.\u002Fllama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest\n```\n\n### AMD GPUs (ROCm)\n\n```bash\n# Build with ROCm\nmake LLAMA_HIP=1\n\n# Run with AMD GPU\n.\u002Fllama-cli -m model.gguf -ngl 999\n```\n\n## Common patterns\n\n### Batch processing\n\n```bash\n# Process multiple prompts from file\ncat prompts.txt | .\u002Fllama-cli \\\n    -m model.gguf \\\n    --batch-size 512 \\\n    -n 100\n```\n\n### Constrained generation\n\n```bash\n# JSON output with grammar\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -p \"Generate a person: \" \\\n    --grammar-file grammars\u002Fjson.gbnf\n\n# Outputs valid JSON only\n```\n\n### Context size\n\n```bash\n# Increase context (default 512)\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -c 4096  # 4K context window\n\n# Very long context (if model supports)\n.\u002Fllama-cli -m model.gguf -c 32768  # 32K context\n```\n\n## Performance benchmarks\n\n### CPU performance (Llama 2-7B Q4_K_M)\n\n| CPU | Threads | Speed | Cost |\n|-----|---------|-------|------|\n| Apple M3 Max | 16 | 50 tok\u002Fs | $0 (local) |\n| AMD Ryzen 9 7950X | 32 | 35 tok\u002Fs | $0.50\u002Fhour |\n| Intel i9-13900K | 32 | 30 tok\u002Fs | $0.40\u002Fhour |\n| AWS c7i.16xlarge | 64 | 40 tok\u002Fs | $2.88\u002Fhour |\n\n### GPU acceleration (Llama 2-7B Q4_K_M)\n\n| GPU | Speed | vs CPU | Cost |\n|-----|-------|--------|------|\n| NVIDIA RTX 4090 | 120 tok\u002Fs | 3-4× | $0 (local) |\n| NVIDIA A10 | 80 tok\u002Fs | 2-3× | $1.00\u002Fhour |\n| AMD MI250 | 70 tok\u002Fs | 2× | $2.00\u002Fhour |\n| Apple M3 Max (Metal) | 50 tok\u002Fs | ~Same | $0 (local) |\n\n## Supported models\n\n**LLaMA family**:\n- Llama 2 (7B, 13B, 70B)\n- Llama 3 (8B, 70B, 405B)\n- Code Llama\n\n**Mistral family**:\n- Mistral 7B\n- Mixtral 8x7B, 8x22B\n\n**Other**:\n- Falcon, BLOOM, GPT-J\n- Phi-3, Gemma, Qwen\n- LLaVA (vision), Whisper (audio)\n\n**Find models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n\n## References\n\n- **[Quantization Guide](references\u002Fquantization.md)** - GGUF formats, conversion, quality comparison\n- **[Server Deployment](references\u002Fserver.md)** - API endpoints, Docker, monitoring\n- **[Optimization](references\u002Foptimization.md)** - Performance tuning, hybrid CPU+GPU\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fllama-cpp\n\n\n","# llama.cpp\n\nPure C\u002FC++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.\n\n## When to use llama.cpp\n\n**Use llama.cpp when:**\n- Running on CPU-only machines\n- Deploying on Apple Silicon (M1\u002FM2\u002FM3\u002FM4)\n- Using AMD or Intel GPUs (no CUDA)\n- Edge deployment (Raspberry Pi, embedded systems)\n- Need simple deployment without Docker\u002FPython\n\n**Use TensorRT-LLM instead when:**\n- Have NVIDIA GPUs (A100\u002FH100)\n- Need maximum throughput (100K+ tok\u002Fs)\n- Running in datacenter with CUDA\n\n**Use vLLM instead when:**\n- Have NVIDIA GPUs\n- Need Python-first API\n- Want PagedAttention\n\n## Quick start\n\n### Installation\n\n```bash\n# macOS\u002FLinux\nbrew install llama.cpp\n\n# Or build from source\ngit clone https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\ncd llama.cpp\nmake\n\n# With Metal (Apple Silicon)\nmake LLAMA_METAL=1\n\n# With CUDA (NVIDIA)\nmake LLAMA_CUDA=1\n\n# With ROCm (AMD)\nmake LLAMA_HIP=1\n```\n\n### Download model\n\n```bash\n# Download from HuggingFace (GGUF format)\nhuggingface-cli download \\\n    TheBloke\u002FLlama-2-7B-Chat-GGUF \\\n    llama-2-7b-chat.Q4_K_M.gguf \\\n    --local-dir models\u002F\n\n# Or convert from HuggingFace\npython convert_hf_to_gguf.py models\u002Fllama-2-7b-chat\u002F\n```\n\n### Run inference\n\n```bash\n# Simple chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    -p \"Explain quantum computing\" \\\n    -n 256  # Max tokens\n\n# Interactive chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --interactive\n```\n\n### Server mode\n\n```bash\n# Start OpenAI-compatible server\n.\u002Fllama-server \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 32  # Offload 32 layers to GPU\n\n# Client request\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"llama-2-7b-chat\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Quantization formats\n\n### GGUF format overview\n\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\n|--------|------|-----------|-------|---------|----------|\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\n\n### Choosing quantization\n\n```bash\n# General use (balanced)\nQ4_K_M  # 4-bit, medium quality\n\n# Maximum speed (more degradation)\nQ2_K or Q3_K_M\n\n# Maximum quality (slower)\nQ6_K or Q8_0\n\n# Very large models (70B, 405B)\nQ3_K_M or Q4_K_S  # Lower bits to fit in memory\n```\n\n## Hardware acceleration\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake LLAMA_METAL=1\n\n# Run with GPU acceleration (automatic)\n.\u002Fllama-cli -m model.gguf -ngl 999  # Offload all layers\n\n# Performance: M3 Max 40-60 tokens\u002Fsec (Llama 2-7B Q4_K_M)\n```\n\n### NVIDIA GPUs (CUDA)\n\n```bash\n# Build with CUDA\nmake LLAMA_CUDA=1\n\n# Offload layers to GPU\n.\u002Fllama-cli -m model.gguf -ngl 35  # Offload 35\u002F40 layers\n\n# Hybrid CPU+GPU for large models\n.\u002Fllama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest\n```\n\n### AMD GPUs (ROCm)\n\n```bash\n# Build with ROCm\nmake LLAMA_HIP=1\n\n# Run with AMD GPU\n.\u002Fllama-cli -m model.gguf -ngl 999\n```\n\n## Common patterns\n\n### Batch processing\n\n```bash\n# Process multiple prompts from file\ncat prompts.txt | .\u002Fllama-cli \\\n    -m model.gguf \\\n    --batch-size 512 \\\n    -n 100\n```\n\n### Constrained generation\n\n```bash\n# JSON output with grammar\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -p \"Generate a person: \" \\\n    --grammar-file grammars\u002Fjson.gbnf\n\n# Outputs valid JSON only\n```\n\n### Context size\n\n```bash\n# Increase context (default 512)\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -c 4096  # 4K context window\n\n# Very long context (if model supports)\n.\u002Fllama-cli -m model.gguf -c 32768  # 32K context\n```\n\n## Performance benchmarks\n\n### CPU performance (Llama 2-7B Q4_K_M)\n\n| CPU | Threads | Speed | Cost |\n|-----|---------|-------|------|\n| Apple M3 Max | 16 | 50 tok\u002Fs | $0 (local) |\n| AMD Ryzen 9 7950X | 32 | 35 tok\u002Fs | $0.50\u002Fhour |\n| Intel i9-13900K | 32 | 30 tok\u002Fs | $0.40\u002Fhour |\n| AWS c7i.16xlarge | 64 | 40 tok\u002Fs | $2.88\u002Fhour |\n\n### GPU acceleration (Llama 2-7B Q4_K_M)\n\n| GPU | Speed | vs CPU | Cost |\n|-----|-------|--------|------|\n| NVIDIA RTX 4090 | 120 tok\u002Fs | 3-4× | $0 (local) |\n| NVIDIA A10 | 80 tok\u002Fs | 2-3× | $1.00\u002Fhour |\n| AMD MI250 | 70 tok\u002Fs | 2× | $2.00\u002Fhour |\n| Apple M3 Max (Metal) | 50 tok\u002Fs | ~Same | $0 (local) |\n\n## Supported models\n\n**LLaMA family**:\n- Llama 2 (7B, 13B, 70B)\n- Llama 3 (8B, 70B, 405B)\n- Code Llama\n\n**Mistral family**:\n- Mistral 7B\n- Mixtral 8x7B, 8x22B\n\n**Other**:\n- Falcon, BLOOM, GPT-J\n- Phi-3, Gemma, Qwen\n- LLaVA (vision), Whisper (audio)\n\n**Find models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n\n## References\n\n- **[Quantization Guide](references\u002Fquantization.md)** - GGUF formats, conversion, quality 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