{"slug":"lmdeploy","name":"LMDeploy","domain":"github.com","verdict":"As of 2026-07-17, ChatGPT, Claude, Gemini, Grok collectively rank LMDeploy #4 of 7 for llm inference server for high-throughput serving (one of 2 leaderboards it appears on). Source: https://modelsagree.com/product/lmdeploy (modelsagree.com, CC BY 4.0).","best_rank":4,"categories":2,"entries":[{"slug":"best-llm-inference-server-for-high-throughput-serving","title":"Best LLM inference server for high-throughput serving","rank":4,"of":7,"score":6,"appearances":3,"modelRanks":{"ChatGPT":4,"Claude":4,"Gemini":4},"reason":"Strong TurboMind-based throughput, efficient persistent batching and KV-cache management, straightforward OpenAI-compatible deployment, and useful quantization support make it a high-value alternative for supported models.","reasons":[{"model":"ChatGPT","reason":"Strong TurboMind-based throughput, efficient persistent batching and KV-cache management, straightforward OpenAI-compatible deployment, and useful quantization support make it a high-value alternative for supported models."},{"model":"Claude","reason":"TurboMind engine delivers excellent throughput and notably strong low-bit (4-bit AWQ, KV cache quantization) performance, often beating vLLM on Qwen/InternLM-family models; simple to operate with an OpenAI-compatible server, making it strong value for teams serving supported model families on constrained GPU budgets."},{"model":"Gemini","reason":"Outperforms vLLM in raw concurrent batch throughput benchmarks on NVIDIA GPUs by using its highly optimized TurboMind engine. It provides a sweet spot of very high performance and memory efficiency with lower deployment complexity and setup overhead compared to TensorRT-LLM."}],"fixes":[{"model":"ChatGPT","fix":"Its model coverage, ecosystem, and production mindshare remain narrower than vLLM or SGLang."},{"model":"Claude","fix":"Model coverage and community are much narrower than vLLM/SGLang; if your model isn't in TurboMind's supported list you fall back to its slower PyTorch engine."},{"model":"Gemini","fix":"Highly NVIDIA-centric with a much smaller community and ecosystem integration footprint, meaning troubleshooting and standard MLOps pipeline integrations (like Kubernetes operators) require custom work."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-high-throughput-serving.json"},{"slug":"best-open-source-llm-serving-stack","title":"Best open-source LLM serving stack","rank":7,"of":7,"score":1,"appearances":1,"modelRanks":{"ChatGPT":5},"reason":"Strong CUDA inference through TurboMind, useful compression and KV-cache quantization, capable OpenAI-compatible serving, and particularly good support for Qwen-family and vision-language deployments.","reasons":[{"model":"ChatGPT","reason":"Strong CUDA inference through TurboMind, useful compression and KV-cache quantization, capable OpenAI-compatible serving, and particularly good support for Qwen-family and vision-language deployments."}],"fixes":[{"model":"ChatGPT","fix":"Its model, hardware, integration, and operator ecosystem remains narrower and less consistently battle-tested than the leaders."}],"updated":"2026-07-13","rank_history":{"days":["2026-07-11","2026-07-12","2026-07-13"],"ranks":[7,null,6]},"api":"https://modelsagree.com/api/v1/best/best-open-source-llm-serving-stack.json"}],"page":"https://modelsagree.com/product/lmdeploy","check":"https://modelsagree.com/check?q=LMDeploy","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}