{"slug":"llama-cpp","name":"llama.cpp","domain":"github.com","verdict":"As of 2026-07-13, ChatGPT, Claude, Gemini, Grok collectively rank llama.cpp #3 of 7 for local llm runner (one of 4 leaderboards it appears on). Source: https://modelsagree.com/product/llama-cpp (modelsagree.com, CC BY 4.0).","best_rank":3,"categories":4,"brief":{"category":"best-local-llm-runner","title":"Best local LLM runner","rank":3,"of":7,"top":"Ollama","day":"2026-07-17","why":[{"t":"broadest hardware coverage","m":["ChatGPT","Claude","Grok","Gemini"],"q":"broadest hardware coverage (CUDA, Metal, Vulkan, ROCm, plain CPU)"},{"t":"bare-metal efficiency","m":["ChatGPT","Claude","Grok","Gemini"],"q":"bare-metal efficiency, low-level compilation control, and unmatched portability"},{"t":"total control","m":["ChatGPT","Claude","Grok","Gemini"],"q":"total control over layers offloaded, context, and sampling"},{"t":"foundational high-performance engine","m":["Claude","Grok","Gemini"],"q":"Foundational high-performance engine"}],"gap":[{"t":"one-command setup","m":["ChatGPT","Claude","Grok"],"q":"one-command setup"},{"t":"curated model library","m":["Claude"],"q":"a curated model library with sane quantization defaults"},{"t":"automatic hardware detection","m":["Gemini"],"q":"automatic hardware detection"}],"fix":[{"t":"built-in model management","m":["ChatGPT","Claude","Gemini","Grok"],"q":"no built-in model management/UI"},{"t":"Steeper setup","m":["Claude","Gemini","Grok"],"q":"Steeper setup"},{"t":"not for beginners","m":["Claude","Gemini","Grok"],"q":"not for beginners seeking plug-and-play"}]},"entries":[{"slug":"best-local-llm-runner","title":"Best local LLM runner","rank":3,"of":7,"score":11,"appearances":4,"modelRanks":{"ChatGPT":3,"Claude":3,"Gemini":4,"Grok":3},"reason":"The most versatile inference foundation, delivering excellent quantized performance across CPU, Metal, CUDA, ROCm, Vulkan, and hybrid CPU-GPU setups with unmatched low-level control","reasons":[{"model":"ChatGPT","reason":"The most versatile inference foundation, delivering excellent quantized performance across CPU, Metal, CUDA, ROCm, Vulkan, and hybrid CPU-GPU setups with unmatched low-level control"},{"model":"Claude","reason":"The engine almost everything else wraps — broadest hardware coverage (CUDA, Metal, Vulkan, ROCm, plain CPU), the GGUF quantization ecosystem, day-one community support for new model architectures, and its built-in llama-server gives an OpenAI-compatible endpoint with total control over layers offloaded, context, and sampling; the best performance per watt of tinkering invested."},{"model":"Grok","reason":"Foundational high-performance engine with broadest hardware support (CPU/GPU/edge), latest model format innovations, and superior efficiency/customization for tinkerers and resource-constrained setups; powers many others"},{"model":"Gemini","reason":"The foundational C/C++ inference engine for GGUF, offering bare-metal efficiency, low-level compilation control, and unmatched portability across almost all CPU/GPU hardware architectures."}],"fixes":[{"model":"ChatGPT","fix":"Provide a polished official model-management interface"},{"model":"Claude","fix":"It's a toolkit, not a product — you manage model files, quant choices, and flags yourself, and keeping up with its fast-moving development is a hobby in itself; not for anyone who just wants a model running in five minutes."},{"model":"Gemini","fix":"Steep technical learning curve that requires manual build compilation, command-line usage, and self-managed downloading and quantization of models."},{"model":"Grok","fix":"Steeper setup and no built-in model management/UI; not for beginners seeking plug-and-play"}],"updated":"2026-07-13","rank_history":{"days":["2026-07-12","2026-07-13"],"ranks":[3,3]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"low-level compilation control","q":"low-level compilation control"},{"t":"manual build compilation","q":"requires manual build compilation"},{"t":"self-managed models and quantization","q":"self-managed downloading and quantization of models"}],"dropped":[]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"OpenAI-compatible endpoint","q":"built-in llama-server gives an OpenAI-compatible endpoint"},{"t":"total inference control","q":"total control over layers offloaded, context, and sampling"},{"t":"fast-moving development hobby","q":"keeping up with its fast-moving development is a hobby in itself"}],"dropped":[{"t":"zero-dependency C/C++","q":"zero-dependency C/C++"}]}],"api":"https://modelsagree.com/api/v1/best/best-local-llm-runner.json"},{"slug":"best-llm-inference-server-for-self-hosting","title":"Best LLM inference server for self-hosting","rank":4,"of":7,"score":8,"appearances":4,"modelRanks":{"ChatGPT":3,"Claude":5,"Gemini":4,"Grok":4},"reason":"Best value for modest or heterogeneous hardware: minimal dependencies, excellent GGUF quantization, CPU-GPU hybrid offload, first-class Apple Silicon, CUDA, ROCm, Vulkan, and a capable OpenAI-compatible server with continuous batching","reasons":[{"model":"ChatGPT","reason":"Best value for modest or heterogeneous hardware: minimal dependencies, excellent GGUF quantization, CPU-GPU hybrid offload, first-class Apple Silicon, CUDA, ROCm, Vulkan, and a capable OpenAI-compatible server with continuous batching"},{"model":"Gemini","reason":"The foundation of modern local inference, offering unparalleled portability and efficiency. Written in pure C/C++, it enables high-performance inference across commodity CPUs, edge devices, and Apple Silicon with minimal footprint, and serves as the backend engine for Ollama."},{"model":"Grok","reason":"Extremely versatile across CPU/GPU/edge hardware, aggressive quantization for low-resource self-hosting, reliable offline server mode with strong community"},{"model":"Claude","reason":"The foundational portable engine — runs essentially everywhere (CPU, Apple Metal, consumer GPUs) with minimal dependencies and best-in-class GGUF quantization, making it the backbone for edge, embedded, and resource-constrained self-hosting."}],"fixes":[{"model":"ChatGPT","fix":"It is not the best choice for high-concurrency datacenter serving or large multi-node GPU clusters, where vLLM and SGLang use accelerators more effectively"},{"model":"Claude","fix":"Concurrent multi-GPU serving throughput trails vLLM/SGLang and it's lower-level/more manual — not the pick for datacenter-scale multi-user serving."},{"model":"Gemini","fix":"Requires manual compilation and command-line configuration, and lacks native distributed multi-GPU/multi-node scaling for large enterprise workloads."},{"model":"Grok","fix":"Enhance multi-user production throughput and dynamic batching for larger concurrent loads"}],"updated":"2026-07-13","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-12","2026-07-13"],"ranks":[6,4,5,4,4,4,4]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"backend engine for Ollama","q":"serves as the backend engine for Ollama"},{"t":"manual compilation and command-line configuration","q":"Requires manual compilation and command-line configuration"},{"t":"lacks native distributed scaling","q":"lacks native distributed multi-GPU/multi-node scaling for large enterprise workloads"}],"dropped":[{"t":"highly optimized GGUF quantizations","q":"highly optimized GGUF quantizations"},{"t":"high-concurrency batching","q":"Improve high-concurrency batching"}]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"edge and embedded backbone","q":"making it the backbone for edge, embedded, and resource-constrained self-hosting"},{"t":"lower-level and manual","q":"it's lower-level/more manual"}],"dropped":[{"t":"OpenAI-compatible endpoint","q":"llama-server gives a lightweight OpenAI-compatible endpoint"}]},{"model":"ChatGPT","from":"2026-07-12","to":"2026-07-13","added":[{"t":"CPU-GPU hybrid offload","q":"CPU-GPU hybrid offload"},{"t":"accelerators used more effectively","q":"vLLM and SGLang use accelerators more effectively"}],"dropped":[{"t":"speculative decoding","q":"speculative decoding"},{"t":"broad API compatibility","q":"broad API compatibility"}]},{"model":"Grok","from":"2026-07-08","to":"2026-07-12","added":[{"t":"strong community","q":"strong community"}],"dropped":[{"t":"static binaries","q":"static binaries"},{"t":"higher GPU utilization","q":"higher GPU utilization"},{"t":"vLLM-class engines","q":"vLLM-class engines"}]}],"api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-self-hosting.json"},{"slug":"best-open-source-llm-serving-stack","title":"Best open-source LLM serving stack","rank":4,"of":7,"score":6,"appearances":3,"modelRanks":{"ChatGPT":4,"Claude":4,"Gemini":4},"reason":"Unmatched value for local, private, edge, CPU, and Apple-silicon serving, with compact deployment, excellent GGUF quantization, broad platform support, and a capable OpenAI-compatible server.","reasons":[{"model":"ChatGPT","reason":"Unmatched value for local, private, edge, CPU, and Apple-silicon serving, with compact deployment, excellent GGUF quantization, broad platform support, and a capable OpenAI-compatible server."},{"model":"Claude","reason":"Unmatched hardware reach — CPU, Apple Silicon, consumer GPUs, edge devices — with the GGUF quantization ecosystem making virtually every open model runnable on commodity hardware; its built-in server exposes an OpenAI-compatible endpoint, and it's the substrate for a huge share of local/on-prem deployments (Ollama, LM Studio build on it)."},{"model":"Gemini","reason":"Unmatched portability and efficiency on consumer hardware, Apple Silicon, and CPU-only systems using highly optimized GGUF quantizations."}],"fixes":[{"model":"ChatGPT","fix":"It is not the strongest option for high-concurrency, multi-node production GPU serving."},{"model":"Claude","fix":"Not a datacenter throughput engine — no serious multi-GPU continuous-batching story at scale, so it's for local, edge, and low-concurrency serving, not high-QPS production APIs."},{"model":"Gemini","fix":"Lack of high-throughput multi-tenant continuous batching and multi-node tensor parallelism needed for large-scale enterprise APIs."}],"updated":"2026-07-13","rank_history":{"days":["2026-07-11","2026-07-12","2026-07-13"],"ranks":[4,4,4]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Optimized GGUF quantizations","q":"using highly optimized GGUF quantizations"},{"t":"Missing multi-node tensor parallelism","q":"Lack of high-throughput multi-tenant continuous batching and multi-node tensor parallelism"}],"dropped":[{"t":"Foundation for edge deployments","q":"serving as the foundation for edge, local, and CPU-based deployments"}]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"OpenAI-compatible endpoint","q":"its built-in server exposes an OpenAI-compatible endpoint"},{"t":"Local tools build on it","q":"Ollama, LM Studio build on it"},{"t":"Huge local/on-prem deployment share","q":"it's the substrate for a huge share of local/on-prem deployments"}],"dropped":[]},{"model":"ChatGPT","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Local and private serving value","q":"Unmatched value for local, private, edge, CPU, and Apple-silicon serving"},{"t":"OpenAI-compatible server","q":"a capable OpenAI-compatible server"}],"dropped":[{"t":"Production-grade autoscaling","q":"Add production-grade multi-node scheduling and autoscaling"}]}],"api":"https://modelsagree.com/api/v1/best/best-open-source-llm-serving-stack.json"},{"slug":"best-llm-inference-server-for-high-throughput-serving","title":"Best LLM inference server for high-throughput serving","rank":6,"of":7,"score":1,"appearances":1,"modelRanks":{"ChatGPT":5},"reason":"Exceptional portability and value across CPUs, Apple Silicon, consumer GPUs, and mixed hardware, with efficient GGUF quantization, continuous batching, speculative decoding, and a capable built-in server.","reasons":[{"model":"ChatGPT","reason":"Exceptional portability and value across CPUs, Apple Silicon, consumer GPUs, and mixed hardware, with efficient GGUF quantization, continuous batching, speculative decoding, and a capable built-in server."}],"fixes":[{"model":"ChatGPT","fix":"It is not the first choice for maximum throughput across large homogeneous datacenter GPU clusters."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-high-throughput-serving.json"}],"page":"https://modelsagree.com/product/llama-cpp","check":"https://modelsagree.com/check?q=llama.cpp","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}