{"slug":"vllm","name":"vLLM","domain":"vllm.ai","verdict":"As of 2026-07-13, ChatGPT, Claude, Gemini, Grok collectively rank vLLM first for llm inference server for self-hosting (one of 8 leaderboards it appears on). Source: https://modelsagree.com/product/vllm (modelsagree.com, CC BY 4.0).","best_rank":1,"categories":8,"entries":[{"slug":"best-llm-inference-server-for-self-hosting","title":"Best LLM inference server for self-hosting","rank":1,"of":7,"score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"Best overall for typical production self-hosting: broad model and accelerator support, excellent continuous batching and KV-cache efficiency, extensive quantization, distributed parallelism, speculative decoding, and mature OpenAI-compatible APIs; near-tied with SGLang, but its wider ecosystem and dependable defaults win","reasons":[{"model":"ChatGPT","reason":"Best overall for typical production self-hosting: broad model and accelerator support, excellent continuous batching and KV-cache efficiency, extensive quantization, distributed parallelism, speculative decoding, and mature OpenAI-compatible APIs; near-tied with SGLang, but its wider ecosystem and dependable defaults win"},{"model":"Claude","reason":"The de facto open-source serving standard — PagedAttention, continuous batching, prefix caching, speculative decoding, and FP8/AWQ/GPTQ quantization give near-best throughput while an OpenAI-compatible server, broad model day-0 support, tensor/pipeline parallelism, and multi-vendor backends (NVIDIA, AMD ROCm) make it the safe production default; near-tie with SGLang at the top."},{"model":"Gemini","reason":"The default industry standard for production serving due to PagedAttention, day-one support for new architectures, and unmatched hardware compatibility across NVIDIA, AMD, TPUs, and CPUs. It is in a near-tie with SGLang, but ranked first due to its superior stability and wider ecosystem integration."},{"model":"Grok","reason":"Highest throughput and memory efficiency via PagedAttention, broad model support, excellent scaling for production concurrent users on GPU clusters, OpenAI-compatible API, rapid feature updates in 2026"}],"fixes":[{"model":"ChatGPT","fix":"Its heavyweight GPU-oriented stack is a poor fit for CPU-only, Apple Silicon, edge, or very small personal deployments"},{"model":"Claude","fix":"Tuning (KV-cache sizing, parallelism) is finicky and it's heavyweight for single-user/local use — not for someone who just wants a model running on a laptop."},{"model":"Gemini","fix":"Lacks SGLang's advanced radix-based prefix caching for repetitive agent prompts and suffers from Python-based runtime overhead at extreme concurrency levels."},{"model":"Grok","fix":"Improve ease of deployment and lower barrier for non-expert users beyond Docker/K8s setups"}],"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":[1,1,1,1,1,1,1]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Unmatched hardware compatibility","q":"unmatched hardware compatibility across NVIDIA, AMD, TPUs, and CPUs"},{"t":"Superior stability over SGLang","q":"ranked first due to its superior stability and wider ecosystem integration"},{"t":"Runtime overhead at extreme concurrency","q":"suffers from Python-based runtime overhead at extreme concurrency levels"}],"dropped":[{"t":"Out-of-box multi-GPU scaling","q":"excellent out-of-the-box multi-GPU scaling"},{"t":"Massive idle VRAM overhead","q":"Reduce its massive idle VRAM overhead"},{"t":"Slow cold-start initialization","q":"speed up cold-start initialization times"}]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Multi-vendor backends","q":"multi-vendor backends (NVIDIA, AMD ROCm)"},{"t":"Finicky KV-cache and parallelism tuning","q":"Tuning (KV-cache sizing, parallelism) is finicky"},{"t":"Heavyweight for local use","q":"it's heavyweight for single-user/local use"}],"dropped":[{"t":"Breaking releases","q":"breaking releases"},{"t":"Multi-node setup demands expertise","q":"multi-node/disaggregated serving setup still demand real expertise versus turnkey rivals"},{"t":"Production track record","q":"production track record from startups to hyperscalers"}]},{"model":"ChatGPT","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Poor fit for small deployments","q":"Its heavyweight GPU-oriented stack is a poor fit for CPU-only, Apple Silicon, edge, or very small personal deployments"}],"dropped":[{"t":"Peak performance needs tuning","q":"Match SGLang and TensorRT-LLM’s peak performance consistently without workload-specific tuning"}]},{"model":"Grok","from":"2026-07-08","to":"2026-07-12","added":[{"t":"Production concurrent user scaling","q":"excellent scaling for production concurrent users on GPU clusters"},{"t":"Rapid feature updates","q":"rapid feature updates in 2026"},{"t":"Lower non-expert barrier","q":"lower barrier for non-expert users"}],"dropped":[{"t":"Broad quantization support","q":"broad model/quantization support (AWQ/GPTQ/FP8)"},{"t":"Largest active community","q":"largest active community"},{"t":"Prefix caching and speculative decoding","q":"more aggressive built-in prefix caching and speculative decoding"}]}],"api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-self-hosting.json"},{"slug":"best-open-source-llm-inference-servers-for-kubernetes","title":"Best open-source LLM inference servers for Kubernetes","rank":1,"of":8,"score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"Best overall default for GPU-backed Kubernetes: excellent throughput, broad model and hardware support, OpenAI-compatible APIs, mature observability, and integrations with KServe, llm-d, Ray Serve, and Dynamo","reasons":[{"model":"ChatGPT","reason":"Best overall default for GPU-backed Kubernetes: excellent throughput, broad model and hardware support, OpenAI-compatible APIs, mature observability, and integrations with KServe, llm-d, Ray Serve, and Dynamo"},{"model":"Claude","reason":"The de facto open-source inference engine for production — best-in-class throughput via PagedAttention and continuous batching, broadest model coverage, and mature Kubernetes deployment paths (Helm charts, the community \"production-stack\", and the Red Hat/Google/IBM-backed llm-d project for distributed prefill/decode disaggregation and KV-cache-aware routing). Assumes the typical practitioner is serving open-weight transformer LLMs at scale on GPUs."},{"model":"Gemini","reason":"Production-standard open-source LLM inference engine offering PagedAttention for high KV-cache efficiency, broad multi-vendor hardware support (NVIDIA, AMD, TPU), native OpenAI API compatibility, and rich Prometheus metrics for KEDA autoscaling in Kubernetes. Near-tie with SGLang for the lead overall position; ranked first due to broader ecosystem adoption, deployment stability, and hardware flexibility across typical Kubernetes infrastructure."},{"model":"Grok","reason":"Broadest model architecture and hardware support (NVIDIA/AMD/TPU/others), mature PagedAttention + continuous batching for high real throughput, native OpenAI-compatible API, official Helm charts plus deep K8s integration via llm-d and KServe, active production adoption and lowest friction to reliable serving for mixed or evolving workloads"}],"fixes":[{"model":"ChatGPT","fix":"The server alone does not provide fleet-level routing, autoscaling, or model lifecycle management, so serious clusters need another control-plane layer"},{"model":"Claude","fix":"The engine itself is not a K8s platform — you assemble autoscaling, routing, and multi-node orchestration yourself (or adopt llm-d, which is young and operationally heavy); config surface is large and tuning is nontrivial."},{"model":"Gemini","fix":"High sensitivity to memory allocation under sudden concurrency spikes, requiring careful manual tuning of GPU memory fractions and queue depth to prevent OOM pod restarts without a wrapper management layer."},{"model":"Grok","fix":"Not the absolute peak single-model NVIDIA throughput after heavy compilation, and prefix-caching less aggressive than SGLang on highly shared-prompt agent/RAG traffic"}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[1,1]},"api":"https://modelsagree.com/api/v1/best/best-open-source-llm-inference-servers-for-kubernetes.json"},{"slug":"best-open-source-llm-serving-stack","title":"Best open-source LLM serving stack","rank":1,"of":7,"score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"Best overall default for production GPU serving: excellent throughput, broad model and hardware support, mature OpenAI-compatible APIs, quantization, speculative decoding, structured outputs, multimodal serving, and scale-out integrations. Ranked first assuming practitioners value compatibility and operability alongside speed.","reasons":[{"model":"ChatGPT","reason":"Best overall default for production GPU serving: excellent throughput, broad model and hardware support, mature OpenAI-compatible APIs, quantization, speculative decoding, structured outputs, multimodal serving, and scale-out integrations. Ranked first assuming practitioners value compatibility and operability alongside speed."},{"model":"Claude","reason":"The de facto standard open-source inference engine — PagedAttention and continuous batching pioneered here, broadest model coverage (day-one support for new releases like Llama, Qwen, DeepSeek variants), widest hardware support (NVIDIA, AMD ROCm, Intel, TPU, AWS Neuron), OpenAI-compatible API, and the largest ecosystem gravity: Kubernetes-native efforts like llm-d and Ray Serve integrations build on it. For the typical practitioner standing up production inference, it has the best docs, community, and hiring pool; assumption: \"typical\" means self-hosting open-weight models on GPUs at moderate-to-large scale. Near-tie with SGLang at #2."},{"model":"Gemini","reason":"Industry-standard GPU production engine with outstanding throughput via PagedAttention, continuous batching, and broad hardware/model support."},{"model":"Grok","reason":"unmatched throughput via PagedAttention, broad model support, OpenAI-compatible API, excellent production scalability and memory efficiency on multi-GPU setups"}],"fixes":[{"model":"ChatGPT","fix":"Rapid development brings regressions and tuning complexity; it is not the lightest choice for edge or CPU-first deployments."},{"model":"Claude","fix":"Rarely the absolute fastest on any given benchmark — on NVIDIA-only fleets a tuned TensorRT-LLM deployment beats it on raw throughput/latency, and its fast-moving codebase means version upgrades occasionally break configs."},{"model":"Gemini","fix":"High memory overhead and requires expensive, dedicated GPU infrastructure, making it unsuitable for local or CPU-only consumer setups."},{"model":"Grok","fix":"deeper native multi-node distributed inference without heavy external orchestration"}],"updated":"2026-07-13","rank_history":{"days":["2026-07-11","2026-07-12","2026-07-13"],"ranks":[1,1,1]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"continuous batching","q":"continuous batching"},{"t":"expensive dedicated GPU infrastructure","q":"requires expensive, dedicated GPU infrastructure"},{"t":"unsuitable for local CPU setups","q":"making it unsuitable for local or CPU-only consumer setups"}],"dropped":[{"t":"extensive ecosystem integration","q":"extensive ecosystem integration"},{"t":"structured output generation speeds","q":"optimize structured output generation speeds"}]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"OpenAI-compatible API","q":"OpenAI-compatible API"},{"t":"best docs and hiring pool","q":"it has the best docs, community, and hiring pool"},{"t":"Rarely the absolute fastest","q":"Rarely the absolute fastest on any given benchmark — on NVIDIA-only fleets a tuned TensorRT-LLM deployment beats it on raw throughput/latency"}],"dropped":[]},{"model":"ChatGPT","from":"2026-07-12","to":"2026-07-13","added":[{"t":"mature OpenAI-compatible APIs","q":"mature OpenAI-compatible APIs"},{"t":"structured outputs","q":"structured outputs"},{"t":"not the lightest choice","q":"it is not the lightest choice for edge or CPU-first deployments"}],"dropped":[{"t":"Hugging Face compatibility","q":"Hugging Face compatibility"}]}],"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":1,"of":7,"score":19,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":1,"Gemini":1,"Grok":1},"reason":"The de facto standard for high-throughput serving — PagedAttention, continuous batching, prefix caching, speculative decoding, and chunked prefill are mature; broadest model coverage (day-one support for new open-weight releases) and hardware reach (NVIDIA, AMD, TPU, Inferentia, Gaudi); huge production install base means battle-tested OpenAI-compatible serving and the richest ecosystem of deployment tooling (production-stack, Ray Serve, KServe integrations). Ranked first on the assumption the typical practitioner serves varied open-weight models on mixed or NVIDIA hardware and values robustness and community support over the last few percent of throughput.","reasons":[{"model":"Claude","reason":"The de facto standard for high-throughput serving — PagedAttention, continuous batching, prefix caching, speculative decoding, and chunked prefill are mature; broadest model coverage (day-one support for new open-weight releases) and hardware reach (NVIDIA, AMD, TPU, Inferentia, Gaudi); huge production install base means battle-tested OpenAI-compatible serving and the richest ecosystem of deployment tooling (production-stack, Ray Serve, KServe integrations). Ranked first on the assumption the typical practitioner serves varied open-weight models on mixed or NVIDIA hardware and values robustness and community support over the last few percent of throughput."},{"model":"Gemini","reason":"The de facto industry standard for general-purpose high-throughput serving, offering the most stable production experience, the broadest hardware ecosystem support (NVIDIA, AMD, TPU, AWS Trainium, Intel), and a massive community that ensures day-one compatibility with new model architectures. (Near-tie with SGLang, but takes top spot due to superior ecosystem maturity and multi-hardware support)."},{"model":"Grok","reason":"Dominant general-purpose choice in 2026 with mature PagedAttention for superior memory efficiency/high concurrency (often 10-20x better throughput vs naive at scale), continuous batching, broad model/hardware support (NVIDIA/AMD/others), OpenAI-compatible API, active development/community, and strong real-world throughput in mixed workloads (e.g., 9k-11k+ tok/s tuned on clusters). FIX: Not always the absolute peak raw throughput on pure NVIDIA max-optimization scenarios (requires tuning for edge cases)."},{"model":"ChatGPT","reason":"Best overall balance of high throughput, broad model and hardware support, OpenAI-compatible serving, quantization, distributed execution, and a mature production ecosystem; a near-tie with SGLang and the safer default for most teams."}],"fixes":[{"model":"ChatGPT","fix":"Absolute performance can trail a workload-tuned SGLang or TensorRT-LLM deployment."},{"model":"Claude","fix":"Rarely the absolute fastest on any single benchmark — SGLang or TensorRT-LLM often beat it on specific model/GPU combos, and its fast release cadence brings occasional per-release regressions you must pin around."},{"model":"Gemini","fix":"Sub-optimal for complex multi-turn agentic or RAG workloads with heavy prefix overlap, where its prefix caching is less optimized than SGLang's RadixAttention."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-high-throughput-serving.json"},{"slug":"best-model-serving-and-deployment-platform","title":"Best model serving and deployment platform","rank":1,"of":10,"score":15,"appearances":3,"modelRanks":{"Claude":1,"Gemini":1,"Grok":1},"reason":"The de facto open-source standard for LLM inference in 2026 — PagedAttention and continuous batching deliver the best throughput-per-GPU, an OpenAI-compatible server out of the box, and the broadest hardware support (NVIDIA, AMD, TPU, Inferentia); its production-stack/llm-d ecosystem now covers real multi-replica deployment. Assumption shaping the rank: LLM serving is the dominant workload this category serves today. Near-tie with SGLang on raw performance; vLLM wins on ecosystem and deployment maturity.","reasons":[{"model":"Claude","reason":"The de facto open-source standard for LLM inference in 2026 — PagedAttention and continuous batching deliver the best throughput-per-GPU, an OpenAI-compatible server out of the box, and the broadest hardware support (NVIDIA, AMD, TPU, Inferentia); its production-stack/llm-d ecosystem now covers real multi-replica deployment. Assumption shaping the rank: LLM serving is the dominant workload this category serves today. Near-tie with SGLang on raw performance; vLLM wins on ecosystem and deployment maturity."},{"model":"Gemini","reason":"Industry-standard open-source engine for transformer models that delivers unmatched throughput and memory efficiency via PagedAttention and continuous batching, acting as the foundation for modern LLM deployment."},{"model":"Grok","reason":"Exceptional throughput and memory efficiency via PagedAttention/continuous batching for LLM inference; broad HF model support, easy OpenAI-compatible API, rapid community-driven"}],"fixes":[{"model":"Claude","fix":"It is an inference engine, not a full platform — you still bring Kubernetes, autoscaling, and observability yourself, and it does nothing for non-LLM models (vision, tabular, classic ML)."},{"model":"Gemini","fix":"Exclusively supports LLMs and vision-language models, making it entirely useless for classical ML, tabular models, or traditional computer vision."}],"updated":"2026-07-15","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-14","2026-07-15"],"ranks":[null,3,null,3,null,2,2]},"reasoning_shift":[{"model":"Claude","from":"2026-07-09","to":"2026-07-14","added":[{"t":"OpenAI-compatible server","q":"an OpenAI-compatible server out of the box"},{"t":"real multi-replica deployment","q":"its production-stack/llm-d ecosystem now covers real multi-replica deployment"},{"t":"nothing for non-LLM models","q":"it does nothing for non-LLM models (vision, tabular, classic ML)"}],"dropped":[{"t":"near-universal model support","q":"near-universal model support the day weights drop"}]}],"api":"https://modelsagree.com/api/v1/best/best-model-serving-and-deployment-platform.json"},{"slug":"best-batch-inference-api-for-large-scale-llm-processing","title":"Best batch inference API for large-scale LLM processing","rank":3,"of":10,"score":8,"appearances":3,"modelRanks":{"Claude":4,"Gemini":5,"Grok":1},"reason":"Dominant open-source engine for high-throughput continuous batching + PagedAttention; delivers 5-10x cost savings vs managed APIs at scale on self-hosted GPUs (e.g. ~$0.3-0.4/M tokens for Llama 70B); broad model support, active development, excellent concurrency scaling and ecosystem integration; top real-world throughput in 2026 benchmarks for batch workloads.","reasons":[{"model":"Grok","reason":"Dominant open-source engine for high-throughput continuous batching + PagedAttention; delivers 5-10x cost savings vs managed APIs at scale on self-hosted GPUs (e.g. ~$0.3-0.4/M tokens for Llama 70B); broad model support, active development, excellent concurrency scaling and ecosystem integration; top real-world throughput in 2026 benchmarks for batch workloads."},{"model":"Claude","reason":"The open-source default for throughput-optimized batch serving — continuous batching, prefix caching, and quantization support routinely deliver the lowest cost-per-token at sustained scale on open-weight models (Llama, Qwen, DeepSeek); at tens of billions of tokens per month with steady GPU utilization it undercuts every commercial batch API, and you control data residency completely."},{"model":"Gemini","reason":"The gold standard for self-hosted processing that avoids API token costs entirely, leveraging PagedAttention to maximize local GPU memory throughput and hardware efficiency."}],"fixes":[{"model":"Claude","fix":"You operate the fleet — GPU procurement, autoscaling, failure handling, and job orchestration are yours; below serious sustained volume the engineering cost exceeds the 50%-discount managed APIs, and you can't run frontier closed models."},{"model":"Gemini","fix":"Demands significant MLOps expertise to orchestrate, scale, and maintain GPU infrastructure and handle cold starts."},{"model":"Grok","fix":"Requires GPU infra team and ops investment (not for teams without self-hosting capability)."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-batch-inference-api-for-large-scale-llm-processing.json"},{"slug":"best-batch-inference-apis-for-synthetic-data-generation","title":"Best batch inference APIs for synthetic data generation","rank":3,"of":9,"score":8,"appearances":2,"modelRanks":{"Claude":2,"Gemini":2},"reason":"The value leader when you own GPUs and use open weights — zero per-token cost, near-hardware-ceiling throughput via continuous batching and prefix caching (huge for the shared-prompt-template pattern of synthetic data), and total control over sampling, logprobs, and guided/structured decoding; scales to arbitrary volume without a vendor queue.","reasons":[{"model":"Claude","reason":"The value leader when you own GPUs and use open weights — zero per-token cost, near-hardware-ceiling throughput via continuous batching and prefix caching (huge for the shared-prompt-template pattern of synthetic data), and total control over sampling, logprobs, and guided/structured decoding; scales to arbitrary volume without a vendor queue."},{"model":"Gemini","reason":"The open-source standard for self-hosted, high-throughput synthetic data generation, leveraging PagedAttention and offline continuous batching (LLM.generate) for maximum VRAM efficiency and zero per-token markup. Assumes the user operates custom GPU infrastructure to generate data from open-weights models like Llama 3 or DeepSeek."}],"fixes":[{"model":"Claude","fix":"Not an API you call — you provision, shard, and babysit GPUs yourself; useless to anyone without infra or the appetite to run it, and open-model output quality still trails frontier closed models on the hardest tasks."},{"model":"Gemini","fix":"Requires significant engineering overhead for infrastructure management and cannot run proprietary state-of-the-art teacher models."}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[3,null]},"api":"https://modelsagree.com/api/v1/best/best-batch-inference-apis-for-synthetic-data-generation.json"},{"slug":"best-local-llm-runner","title":"Best local LLM runner","rank":4,"of":7,"score":9,"appearances":4,"modelRanks":{"ChatGPT":4,"Claude":4,"Gemini":3,"Grok":4},"reason":"Delivers unmatched high-performance inference throughput using PagedAttention, continuous batching, and tensor parallelism, making it the premier backend for local multi-user services and high-concurrency applications.","reasons":[{"model":"Gemini","reason":"Delivers unmatched high-performance inference throughput using PagedAttention, continuous batching, and tensor parallelism, making it the premier backend for local multi-user services and high-concurrency applications."},{"model":"ChatGPT","reason":"Best for high-throughput local GPU serving, with continuous batching, PagedAttention, prefix caching, extensive quantization, speculative decoding, and strong multi-GPU scaling"},{"model":"Claude","reason":"When \"local\" means a real GPU box rather than a laptop, vLLM is the serious choice — PagedAttention and continuous batching deliver far higher throughput than llama.cpp-family engines, with tensor parallelism for multi-GPU rigs and first-class support for serving full-precision or AWQ/GPTQ models; it's what you graduate to when a local model has to serve a team or an agent fleet. Rank assumes a minority of practitioners have this hardware — for them it'd be higher."},{"model":"Grok","reason":"Superior high-throughput serving with PagedAttention and continuous batching for concurrent requests on GPUs; OpenAI API compatible and production-ready for scaled local deployments"}],"fixes":[{"model":"ChatGPT","fix":"Make consumer-desktop installation and hardware support substantially simpler"},{"model":"Claude","fix":"CUDA-centric and VRAM-hungry with weak low-VRAM/GGUF support — effectively unusable on typical consumer laptops and Macs, and its server-oriented complexity is overkill for single-user chat."},{"model":"Gemini","fix":"High hardware requirement restricted to Linux systems with discrete NVIDIA/AMD GPUs, with no optimizations for Apple Silicon unified memory or CPU-only setups."},{"model":"Grok","fix":"Primarily for GPU/multi-user serving with higher setup complexity; overkill and less efficient for single-user casual laptop use"}],"updated":"2026-07-13","rank_history":{"days":["2026-07-12","2026-07-13"],"ranks":[4,4]},"reasoning_shift":[{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Multi-GPU tensor parallelism","q":"with tensor parallelism for multi-GPU rigs"},{"t":"Full-precision and quantized models","q":"first-class support for serving full-precision or AWQ/GPTQ models"},{"t":"Hardware limits its rank","q":"Rank assumes a minority of practitioners have this hardware — for them it'd be higher."}],"dropped":[{"t":"Proper Windows support","q":"proper Windows/desktop support"}]}],"api":"https://modelsagree.com/api/v1/best/best-local-llm-runner.json"}],"page":"https://modelsagree.com/product/vllm","check":"https://modelsagree.com/check?q=vLLM","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}