{"id":"cmrdhl6lv0023hsazow8wgbj6","slug":"vllm-vs-triton))","title":"vLLM vs Triton Inference Server","shortAnswer":"vLLM is a specialized LLM inference engine optimized for throughput with PagedAttention and dynamic batching, while Triton is a general-purpose inference server supporting multiple model types and frameworks with broader deployment flexibility. vLLM achieves 24x higher throughput for LLMs, but Triton handles diverse workloads including computer vision and traditional ML models.","keyDifferences":[{"label":"Primary Use Case","winner":"b","entityAValue":"Large Language Models only","entityBValue":"Multi-model and multi-framework"},{"label":"Throughput for LLMs (tokens/sec)","winner":"a","entityAValue":"24x baseline (with PagedAttention)","entityBValue":"Baseline performance"},{"label":"Supported Model Types","winner":"b","entityAValue":"LLMs (Llama, GPT, Mistral, etc.)","entityBValue":"LLMs, CNNs, RNNs, transformers, traditional ML"},{"label":"Memory Efficiency","winner":"a","entityAValue":"PagedAttention reduces KV cache by 55-75%","entityBValue":"Standard attention mechanisms"},{"label":"Setup Complexity","winner":"a","entityAValue":"Minimal, LLM-specific configuration","entityBValue":"Moderate, requires model config files"},{"label":"Multi-GPU Distribution","winner":"b","entityAValue":"Tensor parallelism, pipeline parallelism","entityBValue":"Multiple backends (vLLM, TensorRT, PyTorch)"},{"label":"Production Maturity","winner":"tie","entityAValue":"Stable since v0.4 (2024), strong LLM ecosystem","entityBValue":"Mature since 2020, enterprise-tested"}],"verdict":"Choose vLLM if you're deploying LLMs and need maximum throughput, lower latency, and minimal operational overhead—it's purpose-built for this exact use case. Choose Triton if you're running a heterogeneous model ecosystem (LLMs + vision models + classical ML), need vendor-agnostic deployment across frameworks, or require deep integration with Kubernetes/cloud platforms.","category":"software","entities":[{"id":"cmqs8ifns00xsr09qawqn7cnn","slug":"vllm","name":"vLLM","shortDesc":"High-throughput LLM inference engine with paged attention and easy-to-use Python API.","imageUrl":"https://upload.wikimedia.org/wikipedia/en/thumb/2/2d/VLLM.svg/330px-VLLM.svg.png","entityType":"software","position":0,"pros":["PagedAttention reduces KV cache memory by 55-75%, enabling longer context windows","24x higher throughput than baseline implementations through continuous batching","Sub-millisecond latency with dynamic batching for individual requests","Supports 30+ LLM architectures (Llama 2/3, Mistral, Qwen, Baichuan, etc.)","OpenAI-compatible REST API requires zero model code changes"],"cons":["LLM-only focus; cannot serve vision models, recommendation systems, or classical ML","GPU-centric design; CPU inference support is limited"],"bestFor":"Teams deploying production LLM services needing maximum throughput and minimal latency (chatbots, API services, content generation)"},{"id":"cmrask9sj00ao5renfv343u3u","slug":"triton-inference-server","name":"Triton Inference Server","shortDesc":"General-purpose inference server supporting multiple frameworks and model types","imageUrl":null,"entityType":"software","position":1,"pros":["Supports 8+ backends: TensorRT, PyTorch, ONNX Runtime, TensorFlow, vLLM, and more","Unified API for heterogeneous models (LLMs, CNNs, NLP, recommenders, classical ML)","Ensemble pipeline support for multi-model workflows (e.g., embedding → ranking → re-ranking)","Native Kubernetes integration with auto-scaling and health probes","Model versioning and A/B testing built-in"],"cons":["Configuration overhead for each model (model.pbtxt files required)","Lower LLM throughput than vLLM when used standalone without vLLM backend"],"bestFor":"Enterprise teams with mixed workloads, complex inference pipelines, and need for standardized deployment across CPU/GPU/TPU infrastructure"}],"attributes":[{"id":"cmraw1uzr000bibit5e1s7rdz","slug":"peak-throughput-13b-model-v100-","name":"Peak Throughput (13B model, V100)","unit":"tokens/second","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"2800","valueNumber":2800,"valueBoolean":null}]},{"id":"cmraw1v02000hibithc1jbfs8","slug":"memory-usage-13b-model-batch-32-","name":"Memory Usage (13B model, batch=32)","unit":"GB","category":"Resource Efficiency","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"10.5","valueNumber":10.5,"valueBoolean":null}]},{"id":"cmraw1v0b000nibitmtiaa7vr","slug":"time-to-first-token-p99-latency-","name":"Time to First Token (p99 latency)","unit":"milliseconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"45","valueNumber":45,"valueBoolean":null}]},{"id":"cmraw1v0m000tibitq5fz36xb","slug":"setup-time-from-install-to-inference-","name":"Setup Time (from install to inference)","unit":"minutes","category":"Deployment","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"5","valueNumber":5,"valueBoolean":null}]},{"id":"cmraw1v0v000zibit5yjdfn1s","slug":"gpu-platform-support-count","name":"GPU Platform Support Count","unit":"platforms","category":"Compatibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.)","valueNumber":7,"valueBoolean":null}]},{"id":"cmqs8f1wj00pfr09qtuay0ulc","slug":"maximum-concurrent-requests","name":"Maximum Concurrent Requests","unit":"requests","category":"Scalability","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"256","valueNumber":256,"valueBoolean":null}]},{"id":"cmqs8ifog00xzr09qlbce6olk","slug":"time-to-first-token-ms-","name":"Time to First Token (ms)","unit":"milliseconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"80-120 ms","valueNumber":100,"valueBoolean":null}]},{"id":"cmqs8ifos00y5r09qobg3vto1","slug":"throughput-tokens-second-batch-size-32-","name":"Throughput (tokens/second, batch size 32)","unit":"tokens/sec","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~1200 tok/s","valueNumber":1200,"valueBoolean":null}]},{"id":"cmq85t685000fhy7abrp6v5zu","slug":"minimum-ram-required","name":"Minimum RAM Required","unit":"GB","category":"System Requirements","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"8 GB","valueNumber":8,"valueBoolean":null}]},{"id":"cmqs8ifpe00yhr09qlwhgoud5","slug":"gpu-memory-for-7b-model","name":"GPU Memory for 7B Model","unit":"GB","category":"Hardware Requirements","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"5-6 GB (with optimization)","valueNumber":5.5,"valueBoolean":null}]},{"id":"cmqs8ifpq00ynr09qfowzq734","slug":"setup-time-from-download-to-first-inference-","name":"Setup Time (from download to first inference)","unit":"minutes","category":"Usability","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"30 minutes","valueNumber":30,"valueBoolean":null}]},{"id":"cmqs8ifq200ytr09qo7oo3ilg","slug":"pre-packaged-models-available","name":"Pre-packaged Models Available","unit":"count","category":"Model Support","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Unlimited (HuggingFace)","valueNumber":null,"valueBoolean":null}]},{"id":"cmmxr90aj01vvlh9en2wgumc3","slug":"github-stars","name":"GitHub Stars","unit":"stars","category":"Adoption & Community","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"23,000+","valueNumber":23000,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"7,500+","valueNumber":7500,"valueBoolean":null,"winner":false}]},{"id":"cmqs8ifqp00z5r09qgzk805it","slug":"cpu-fallback-support","name":"CPU Fallback Support","unit":"capability","category":"Hardware Compatibility","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Limited, requires GPU","valueNumber":null,"valueBoolean":null}]},{"id":"cmqsb8t85001d11mtt2oucm4h","slug":"throughput-tokens-second-llama-70b-example-","name":"Throughput (tokens/second, LLaMA 70B example)","unit":"tokens/sec","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"1,500+","valueNumber":1500,"valueBoolean":null}]},{"id":"cmqsb8t8n001j11mttm6pxgjh","slug":"kv-cache-memory-usage-reduction","name":"KV Cache Memory Usage Reduction","unit":"x factor","category":"Memory Efficiency","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~4x reduction","valueNumber":4,"valueBoolean":null}]},{"id":"cmqiukcv2002jmzvhjqfme3bw","slug":"supported-ml-frameworks","name":"Supported ML Frameworks","unit":"count","category":"Integrations","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Primarily PyTorch/Transformers (limited)","valueNumber":null,"valueBoolean":null}]},{"id":"cmqsb8t97001v11mt29dr0a5p","slug":"github-stars-community-adoption-metric-","name":"GitHub Stars (community adoption metric)","unit":"stars","category":"Community","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"21,000+","valueNumber":21000,"valueBoolean":null}]},{"id":"cmqsb8t9i002111mtfkuippeg","slug":"minimum-gpu-memory-llama-70b-1-gpu-","name":"Minimum GPU Memory (LLaMA 70B, 1 GPU)","unit":"GB","category":"Hardware Requirements","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"40 GB (with PagedAttention)","valueNumber":40,"valueBoolean":null}]},{"id":"cmqsb8t9s002711mtndf6x81y","slug":"multi-model-serving-setup-complexity","name":"Multi-Model Serving Setup Complexity","unit":"complexity level","category":"Ease of Use","dataType":"text","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"High (requires separate instances)","valueNumber":null,"valueBoolean":null}]},{"id":"cmqsb8ta2002d11mtbs251ypi","slug":"batch-size-improvement-via-memory-savings-","name":"Batch Size Improvement (via memory savings)","unit":"x multiplier","category":"Scalability","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"4x larger batches possible","valueNumber":4,"valueBoolean":null}]},{"id":"cmqsb8tab002j11mt14wfdgao","slug":"distributed-parallelism-setup-time","name":"Distributed Parallelism Setup Time","unit":"minutes to configure","category":"Developer Experience","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"15-30 (built-in helpers)","valueNumber":22,"valueBoolean":null}]},{"id":"cmqsbedxj003b11mtflnla18i","slug":"token-throughput-a100-40gb-7b-model-","name":"Token Throughput (A100-40GB, 7B model)","unit":"tokens/sec","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"12,500 tokens/sec","valueNumber":12500,"valueBoolean":null}]},{"id":"cmqsbedy7003h11mtfjalq09g","slug":"memory-usage-kv-cache-7b-model-batch-1-","name":"Memory Usage (KV cache, 7B model, batch=1)","unit":"GB","category":"Efficiency","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"8.2 GB (with PagedAttention)","valueNumber":8.2,"valueBoolean":null}]},{"id":"cmqsbedyn003n11mt97zm3oag","slug":"supported-model-frameworks","name":"Supported Model Frameworks","unit":"count","category":"Compatibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"2 (LLM-specific)","valueNumber":2,"valueBoolean":null,"winner":false},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"6+ frameworks","valueNumber":6,"valueBoolean":null,"winner":true}]},{"id":"cmqsbedz2003t11mtbn8tyip4","slug":"p99-latency-7b-model-batch-32-","name":"P99 Latency (7B model, batch=32)","unit":"milliseconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"380 ms","valueNumber":380,"valueBoolean":null}]},{"id":"cmnbmr9vf0165slg4tbspfayv","slug":"configuration-complexity","name":"Configuration Complexity","unit":"config parameters","category":"Operational","dataType":"text","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Low (Python API)","valueNumber":null,"valueBoolean":null},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"High (YAML+Protobuf)","valueNumber":null,"valueBoolean":null}]},{"id":"cmqsbedzw004511mto6okwbkv","slug":"model-ensemble-support","name":"Model Ensemble Support","unit":"boolean","category":"Features","dataType":"text","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"No native ensemble; requires external orchestration","valueNumber":null,"valueBoolean":null}]},{"id":"cmqsbee0b004b11mt2hillz9n","slug":"production-users-estimated-","name":"Production Users (Estimated)","unit":"organizations","category":"Enterprise Adoption","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~1,200+ organizations (LLM-focused)","valueNumber":1200,"valueBoolean":null}]},{"id":"cmqdjvzj8000uxqy53bn9e77u","slug":"github-stars-as-of-2026-","name":"GitHub Stars (as of 2026)","unit":"thousands","category":"Community Adoption","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~24,000","valueNumber":24000,"valueBoolean":null}]},{"id":"cmqsbi17u005911mtq6raivlm","slug":"throughput-tokens-sec-on-a100-","name":"Throughput (tokens/sec on A100)","unit":"tokens/second","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~8,000-12,000","valueNumber":10000,"valueBoolean":null}]},{"id":"cmqsbi18g005f11mtlio0tunu","slug":"per-token-latency-llama-2-70b-","name":"Per-Token Latency (Llama 2 70B)","unit":"milliseconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"50-60ms","valueNumber":55,"valueBoolean":null}]},{"id":"cmqsbi18t005l11mt0wlz1af8","slug":"supported-gpu-platforms","name":"Supported GPU Platforms","unit":"number of platforms","category":"Compatibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"NVIDIA, AMD, Intel, CPU (4 platforms)","valueNumber":4,"valueBoolean":null}]},{"id":"cmqsbi196005r11mtzu8tymph","slug":"pre-optimized-model-count","name":"Pre-optimized Model Count","unit":"models","category":"Model Support","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"500+ with auto-optimization","valueNumber":500,"valueBoolean":null}]},{"id":"cmqsbi19k005x11mtpyz1iijj","slug":"memory-usage-reduction-vs-pytorch-","name":"Memory Usage Reduction (vs PyTorch)","unit":"percent","category":"Efficiency","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"50-60% (Paged Attention)","valueNumber":55,"valueBoolean":null}]},{"id":"cmqsbi1ab006911mt7akb6mcj","slug":"setup-time-basic-deployment-","name":"Setup Time (Basic Deployment)","unit":"minutes","category":"Deployment","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"5-10 minutes","valueNumber":7,"valueBoolean":null}]},{"id":"cmonyduie000x10tjevwavq8d","slug":"cost","name":"Cost","unit":"USD","category":"Economics","dataType":"text","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Free (open-source)","valueNumber":0,"valueBoolean":null}]},{"id":"cmqsbjq37007711mt3ev7jrkt","slug":"inference-throughput-single-a100-gpu-","name":"Inference Throughput (single A100 GPU)","unit":"tokens/second","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"25,000 tokens/sec","valueNumber":25000,"valueBoolean":null}]},{"id":"cmqsbjq3n007d11mt4frjiua2","slug":"setup-time-basic-inference-","name":"Setup Time (basic inference)","unit":"minutes","category":"Ease of Use","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"120-420 minutes (2-7 days with infrastructure)","valueNumber":240,"valueBoolean":null}]},{"id":"cmqsbjq41007j11mt56ptg3pe","slug":"cost-per-million-tokens-a100-on-demand-","name":"Cost per Million Tokens (A100, on-demand)","unit":"USD","category":"Cost","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"$0.12","valueNumber":0.12,"valueBoolean":null}]},{"id":"cmqsbjq4j007p11mt7g9nafu5","slug":"supported-models-major-open-source-","name":"Supported Models (major open-source)","unit":"count","category":"Ecosystem","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"1,000+ models","valueNumber":1000,"valueBoolean":null}]},{"id":"cmqsbjq4x007v11mt6hw3x6sf","slug":"training-capabilities","name":"Training Capabilities","unit":null,"category":"Features","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Inference-only, no native training","valueNumber":null,"valueBoolean":null}]},{"id":"cmqo4wti10049dj1h5kmyqt2o","slug":"enterprise-sla-uptime","name":"Enterprise SLA Uptime","unit":"percent","category":"Reliability","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Community-dependent (typically 99.0%+)","valueNumber":99,"valueBoolean":null}]},{"id":"cmqsbjq6s008711mt3ib6l61q","slug":"infrastructure-management","name":"Infrastructure Management","unit":null,"category":"Operations","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"User-managed (CUDA, Docker, scaling)","valueNumber":null,"valueBoolean":null}]},{"id":"cmqsbjq7c008d11mt0ww0rndi","slug":"community-documentation","name":"Community & Documentation","unit":"GitHub stars","category":"Support","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"25,000+ stars, weekly updates","valueNumber":25000,"valueBoolean":null}]},{"id":"cmrdhl6mg0029hsaza1m8qcuw","slug":"llm-throughput-tokens-sec-","name":"LLM Throughput (tokens/sec)","unit":"tokens/sec","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~5,000-15,000 (on A100 with batching)","valueNumber":10000,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"~200-500 (baseline without vLLM backend)","valueNumber":350,"valueBoolean":null,"winner":false}]},{"id":"cmrdhl6mt002fhsazxl8bbihi","slug":"kv-cache-memory-usage","name":"KV Cache Memory Usage","unit":"% of baseline","category":"Memory Efficiency","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"25-45% of baseline","valueNumber":35,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"100% (standard attention)","valueNumber":100,"valueBoolean":null,"winner":false}]},{"id":"cmrdhl6n3002lhsazenryx06l","slug":"supported-model-categories","name":"Supported Model Categories","unit":"count","category":"Flexibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"LLMs only (30+ architectures)","valueNumber":30,"valueBoolean":null,"winner":false},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"8+ model categories with 10+ frameworks","valueNumber":80,"valueBoolean":null,"winner":true}]},{"id":"cmraw2gir002jibitxc1iexle","slug":"openai-api-compatibility","name":"OpenAI API Compatibility","unit":"boolean","category":"Developer Experience","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Native support, drop-in replacement","valueNumber":null,"valueBoolean":null},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"Requires wrapper/plugin","valueNumber":null,"valueBoolean":null}]},{"id":"cmrdhl6nz0033hsaz1hsp4hus","slug":"enterprise-maturity-years-in-production-","name":"Enterprise Maturity (years in production)","unit":"years","category":"Stability","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"2 years (v1.0 in 2024)","valueNumber":2,"valueBoolean":null,"winner":false},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"6 years (v1.0 in 2020)","valueNumber":6,"valueBoolean":null,"winner":true}]},{"id":"cmrdhl6o90039hsazhlkn9xi3","slug":"gpu-memory-for-13b-llm","name":"GPU Memory for 13B LLM","unit":"GB","category":"Resource Requirements","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~18-22 GB with continuous batching","valueNumber":20,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"~24-28 GB standard","valueNumber":26,"valueBoolean":null,"winner":false}]},{"id":"cmqb6yyz00036onv2c6pnx5pt","slug":"kubernetes-native-support","name":"Kubernetes Native Support","unit":"boolean","category":"Deployment","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Community Helm charts available","valueNumber":null,"valueBoolean":null},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"Official Helm charts and auto-scaling","valueNumber":null,"valueBoolean":null}]},{"id":"cmrapkovq00h1iuhoxin8jys8","slug":"inference-throughput-rtx-4090-llama-2-13b-","name":"Inference Throughput (RTX 4090, Llama 2 13B)","unit":"tokens/second","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~700 tokens/sec","valueNumber":700,"valueBoolean":null}]},{"id":"cmrapkow300h7iuho4ugs8e75","slug":"memory-usage-llama-2-7b-quantized-","name":"Memory Usage (Llama 2 7B, quantized)","unit":"GB","category":"Resource Usage","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~6.5 GB","valueNumber":6.5,"valueBoolean":null}]},{"id":"cmrapkowg00hdiuho6vyb7m7y","slug":"installation-time-from-zero-","name":"Installation Time (from zero)","unit":"minutes","category":"Ease of Use","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"25-40 minutes","valueNumber":32,"valueBoolean":null}]},{"id":"cmrapkowq00hjiuhobx30cj96","slug":"minimum-vram-for-llama-2-7b","name":"Minimum VRAM for Llama 2 7B","unit":"GB","category":"Hardware Requirements","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"6 GB","valueNumber":6,"valueBoolean":null}]},{"id":"cmrapkox000hpiuhogx5ka1po","slug":"number-of-supported-gpu-backends","name":"Number of Supported GPU Backends","unit":"count","category":"Compatibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"4+ (CUDA, ROCm, CPU, TPU, custom)","valueNumber":5,"valueBoolean":null}]},{"id":"cmrapkoxc00hviuhoa8zrzxwn","slug":"batch-processing-support","name":"Batch Processing Support","unit":"null","category":"Features","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Yes (native continuous batching)","valueNumber":null,"valueBoolean":null}]},{"id":"cmqi58cpc002hdlw4bvicvapo","slug":"api-standardization","name":"API Standardization","unit":"null","category":"Integration","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"OpenAI-compatible API","valueNumber":null,"valueBoolean":null}]},{"id":"cmrask9td00av5ren8hj4sry7","slug":"llm-throughput-improvement","name":"LLM Throughput Improvement","unit":"x faster than baseline","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"24x","valueNumber":24,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"6x","valueNumber":6,"valueBoolean":null,"winner":false}]},{"id":"cmrask9tt00b15renfiyhq0n5","slug":"memory-usage-kv-cache-","name":"Memory Usage (KV Cache)","unit":"% reduction vs standard","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"80% reduction","valueNumber":80,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"30% reduction","valueNumber":30,"valueBoolean":null,"winner":false}]},{"id":"cmrask9us00bj5ren410uewqj","slug":"enterprise-deployment-features","name":"Enterprise Deployment Features","unit":"feature count","category":"Enterprise","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"3 (basic)","valueNumber":3,"valueBoolean":null,"winner":false},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"8+ (advanced)","valueNumber":8,"valueBoolean":null,"winner":true}]},{"id":"cmrask9v400bp5ren8swgd6za","slug":"multi-gpu-support","name":"Multi-GPU Support","unit":"scaling efficiency","category":"Scalability","dataType":"text","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Native (tensor parallelism)","valueNumber":null,"valueBoolean":null},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"Supported (requires config)","valueNumber":null,"valueBoolean":null}]},{"id":"cmqgsxroz0055blry1kfdsyut","slug":"production-monitoring","name":"Production Monitoring","unit":"metrics exported","category":"Operations","dataType":"text","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Basic (throughput, latency)","valueNumber":null,"valueBoolean":null},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"Advanced (Prometheus, tracing)","valueNumber":null,"valueBoolean":null}]},{"id":"cmrbcf1in018pg9dsr22taq8y","slug":"inference-throughput-llama-2-70b-a100-gpu-","name":"Inference Throughput (LLaMA 2 70B, A100 GPU)","unit":"tokens/sec","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"25,000 tokens/sec (batch 256)","valueNumber":25000,"valueBoolean":null}]},{"id":"cmrbcf1iz018vg9dskk12ubj6","slug":"memory-usage-llama-2-70b-","name":"Memory Usage (LLaMA 2 70B)","unit":"GB","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"45 GB (with PagedAttention)","valueNumber":45,"valueBoolean":null}]},{"id":"cmq9xuv49002p11c7omgny8bm","slug":"deployment-time","name":"Deployment Time","unit":"seconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"20-30 minutes (self-hosted)","valueNumber":25,"valueBoolean":null}]},{"id":"cmrbcf1jm0197g9dsa4if5xee","slug":"cost-per-1m-tokens-llama-2-70b-on-demand-","name":"Cost per 1M Tokens (LLaMA 2 70B, On-Demand)","unit":"USD","category":"Cost","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"$0.25 (self-hosted, amortized)","valueNumber":0.25,"valueBoolean":null}]},{"id":"cmrbcf1jx019dg9dsgwzqm93z","slug":"model-support-open-source-llms-","name":"Model Support (Open-Source LLMs)","unit":"models","category":"Flexibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"500+ community models","valueNumber":500,"valueBoolean":null}]},{"id":"cmqp1bihr012t13o1asrbrk5c","slug":"infrastructure-management-required","name":"Infrastructure Management Required","unit":"null","category":"Operations","dataType":"text","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"User-managed (Docker, K8s, VMs)","valueNumber":null,"valueBoolean":null}]},{"id":"cmrbcf1kj019pg9ds90k3ke0d","slug":"sla-availability-guarantee","name":"SLA Availability Guarantee","unit":"%","category":"Reliability","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"No SLA (community support)","valueNumber":null,"valueBoolean":null}]},{"id":"cmnf2jm3800wv2s3jpiu2p2av","slug":"enterprise-support-availability","name":"Enterprise Support Availability","unit":null,"category":"Support & Licensing","dataType":"text","higherIsBetter":null,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Community (GitHub issues)","valueNumber":null,"valueBoolean":null}]},{"id":"cmrau1i2t0083h415h2q0yyrr","slug":"gpu-memory-reduction-vs-baseline","name":"GPU Memory Reduction vs Baseline","unit":"%","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"~60%","valueNumber":60,"valueBoolean":null}]},{"id":"cmrau1i370089h4152a150rpg","slug":"throughput-improvement-batching-","name":"Throughput Improvement (Batching)","unit":"x improvement","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"10-23x vs standard","valueNumber":16.5,"valueBoolean":null}]},{"id":"cmqsb37ag000f11mtzmk9taws","slug":"supported-model-formats","name":"Supported Model Formats","unit":"formats","category":"Compatibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"15+ formats (HF, GGUF, AWQ, GPTQ, etc)","valueNumber":15,"valueBoolean":null}]},{"id":"cmrau1i4f008rh415o4qk6heq","slug":"time-to-deploy-minutes-","name":"Time to Deploy (minutes)","unit":"minutes","category":"Ease of Use","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"5-10 minutes for basic LLM service","valueNumber":7,"valueBoolean":null,"winner":true},{"entityId":"cmrask9sj00ao5renfv343u3u","valueText":"20-30 minutes with model config setup","valueNumber":25,"valueBoolean":null,"winner":false}]},{"id":"cmrau1i4t008xh415ev8mqv5q","slug":"token-streaming-native-support","name":"Token Streaming Native Support","unit":"boolean","category":"Features","dataType":"text","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Via API wrapper","valueNumber":null,"valueBoolean":null}]},{"id":"cmrau1i570093h415pm93u2s3","slug":"official-enterprise-support","name":"Official Enterprise Support","unit":"boolean","category":"Support","dataType":"text","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"Community-based","valueNumber":null,"valueBoolean":null}]},{"id":"cmrau1i5k0099h415zctnvtoj","slug":"latest-version-release-cycle","name":"Latest Version Release Cycle","unit":"weeks","category":"Maintenance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"2-3 weeks","valueNumber":2.5,"valueBoolean":null}]},{"id":"cmrcgpid50029iipz1jxnq3jn","slug":"inference-throughput-tokens-sec-llama-2-7b-","name":"Inference Throughput (tokens/sec, Llama 2 7B)","unit":"tokens/sec","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"2,000-3,500 tokens/sec","valueNumber":2750,"valueBoolean":null}]},{"id":"cmrcgpidh002fiipzybws84px","slug":"time-to-first-token-p50-latency-","name":"Time to First Token (p50 latency)","unit":"milliseconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"50-150ms","valueNumber":100,"valueBoolean":null}]},{"id":"cmrcgpidp002liipzj8wq7cqs","slug":"model-type-support-count","name":"Model Type Support Count","unit":"categories","category":"Flexibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"1 (LLMs only)","valueNumber":1,"valueBoolean":null}]},{"id":"cmrcgpidw002riipzlzrye4oe","slug":"memory-usage-llama-2-13b-fp16-","name":"Memory Usage (Llama 2 13B fp16)","unit":"GB","category":"Efficiency","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"24-28 GB","valueNumber":26,"valueBoolean":null}]},{"id":"cmqs8hzf800ufr09qfld8xoc9","slug":"supported-quantization-formats","name":"Supported Quantization Formats","unit":"count","category":"Features","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"GPTQ, AWQ, INT4, FP8, INT8","valueNumber":5,"valueBoolean":null}]},{"id":"cmrcgpieb0033iipzcmehxepv","slug":"multi-gpu-scaling-efficiency-8-gpu-","name":"Multi-GPU Scaling Efficiency (8 GPU)","unit":"percent","category":"Scalability","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"92% linear scaling","valueNumber":92,"valueBoolean":null}]},{"id":"cmrcgpiei0039iipzf6qyg55o","slug":"max-concurrent-requests-typical-setup-","name":"Max Concurrent Requests (typical setup)","unit":"requests","category":"Capacity","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"500-2,000 concurrent","valueNumber":1250,"valueBoolean":null}]},{"id":"cmrdlgwzg00hn458vs5fesri2","slug":"time-to-first-inference","name":"Time to First Inference","unit":"minutes","category":"Deployment","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"2-3 minutes","valueNumber":2.5,"valueBoolean":null}]},{"id":"cmrdlgwzt00ht458vr3cejao1","slug":"throughput-7b-model-a100-","name":"Throughput (7B model, A100)","unit":"tokens/second","category":"Performance","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"3,200 tokens/sec","valueNumber":3200,"valueBoolean":null}]},{"id":"cmrdlgx0500hz458vqxofz186","slug":"latency-per-token-batch-1-7b-model-","name":"Latency per Token (batch=1, 7B model)","unit":"milliseconds","category":"Performance","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"45 ms","valueNumber":45,"valueBoolean":null}]},{"id":"cmrdlgx0g00i5458v3vje6q3n","slug":"model-architecture-support","name":"Model Architecture Support","unit":"count","category":"Compatibility","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"30+ architectures","valueNumber":30,"valueBoolean":null}]},{"id":"cmosye4lt001bt759qh27pjq5","slug":"installation-complexity","name":"Installation Complexity","unit":"steps required","category":"Setup","dataType":"text","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"1-2 commands","valueNumber":1.5,"valueBoolean":null}]},{"id":"cmrdlgx1500ih458vsbkg3h6j","slug":"vram-usage-7b-model-int8-","name":"VRAM Usage (7B model, INT8)","unit":"GB","category":"Resource Efficiency","dataType":"number","higherIsBetter":false,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"9-11 GB","valueNumber":10,"valueBoolean":null}]},{"id":"cmpikd78i001cms5zawa547yc","slug":"github-stars-2026-","name":"GitHub Stars (2026)","unit":"stars","category":"Community","dataType":"number","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"22,000+ stars","valueNumber":22000,"valueBoolean":null}]},{"id":"cmraw1v1l001bibitskrlw29o","slug":"quantization-method-support","name":"Quantization Method Support","unit":"count","category":"Features","dataType":"text","higherIsBetter":true,"values":[{"entityId":"cmqs8ifns00xsr09qawqn7cnn","valueText":"5 methods (GPTQ, AWQ, SqueezeLLM, etc.)","valueNumber":5,"valueBoolean":null}]}],"faqs":[{"question":"Can I use vLLM as a backend in Triton?","answer":"Yes. Triton added official vLLM backend support in v2.34+. This combines Triton's multi-model orchestration with vLLM's LLM optimization, allowing you to run heterogeneous workloads where LLM inference uses vLLM and other models use appropriate backends."},{"question":"Does vLLM support vision-language models?","answer":"vLLM added experimental support for vision-language models (LLaVA, Qwen-VL) in v0.4, but optimization is focused on the language component. For production vision inference, Triton with TensorRT backend is more mature."},{"question":"What's the latency difference between vLLM and Triton for a single request?","answer":"For a single LLM request: vLLM ~150-300ms, Triton with vLLM backend ~200-350ms (slight overhead for routing). For batched requests (10+), vLLM achieves 50x lower latency per token due to continuous batching versus traditional request queuing."},{"question":"Which should I use for an LLM-only service?","answer":"Use vLLM. It requires less configuration, deploys faster (5-10 minutes), consumes 20-30% less GPU memory, and delivers 24x higher throughput. Triton adds unnecessary complexity if you're not running multiple model types."},{"question":"Can I do multi-model inference with vLLM?","answer":"vLLM is single-model-focused per process. For multi-model inference, run multiple vLLM instances (one per model) or use Triton with vLLM backend to orchestrate multiple models with shared resources and load balancing."}],"relatedComparisons":[{"slug":"vllm-vs-triton)","title":"vLLM vs Triton Inference Server","category":"software"},{"slug":"ollama-vs-vllm","title":"Ollama vs vLLM","category":"software"},{"slug":"vllm-vs-ray-serve","title":"vLLM vs Ray Serve","category":"software"},{"slug":"vllm-vs-triton","title":"vLLM vs Triton Inference Server","category":"software"},{"slug":"vllm-vs-tensorrt-llm","title":"vLLM vs TensorRT-LLM","category":"software"},{"slug":"vllm-vs-sagemaker","title":"vLLM vs Amazon SageMaker","category":"software"},{"slug":"ollama-vs-vllm)","title":"Ollama vs vLLM","category":"software"},{"slug":"vllm-vs-tgi)","title":"vLLM vs Text Generation Inference (TGI)","category":"software"},{"slug":"vllm-vs-tensorrt-llm)","title":"vLLM vs TensorRT-LLM","category":"software"},{"slug":"vllm-vs-sagemaker)","title":"vLLM vs Amazon SageMaker","category":"software"},{"slug":"vllm-vs-ray-serve)","title":"vLLM vs Ray Serve","category":"software"},{"slug":"vllm-vs-tensorrt-llm))","title":"vLLM vs TensorRT-LLM","category":"software"}],"relatedBlogPosts":[{"slug":"best-streaming-services-in-2026-top-picks-for-every-budget-interest","title":"Best Streaming Services in 2026: Top Picks for Every Budget & Interest","excerpt":"Navigating the crowded streaming landscape in 2026 can be overwhelming. We've tested and ranked the best streaming services that offer the most value, from Netflix's massive library to budget-friendly options like Tubi, helping you cut cable and find your perfect entertainment solution.","category":"technology"},{"slug":"best-live-tv-streaming-services-plans-for-spring-2026-complete-buyers-guide","title":"Best Live TV Streaming Services & Plans for Spring 2026: Complete Buyer's Guide","excerpt":"Tired of overpaying for cable? Discover the best live TV streaming services and plans for Spring 2026, including YouTube TV's new genre-based packages starting at $55/month. Our comprehensive guide breaks down pricing, channels, and features to help you cut the cord.","category":"technology"},{"slug":"philo-in-2026-streaming-tv-service-review-pricing-reddit-community-insights","title":"Philo in 2026: Streaming TV Service Review, Pricing & Reddit Community Insights","excerpt":"Explore Philo's evolution heading into 2026, including pricing tiers, channel lineup, and how it compares to competitors like Sling TV. Discover what the r/PhiloTV Reddit community thinks about the service's current offerings and future prospects.","category":"technology"},{"slug":"best-us-fighter-jets-2026-top-american-combat-aircraft-ranked","title":"Best US Fighter Jets 2026: Top American Combat Aircraft Ranked","excerpt":"Discover the most advanced US fighter jets dominating the skies in 2026. From the legendary F-22 Raptor to the versatile F-35 Lightning II, we rank America's best combat aircraft based on performance, stealth, and air superiority capabilities.","category":"technology"},{"slug":"philo-in-2026-pricing-lineup-how-it-compares-to-sling-tv","title":"Philo in 2026: Pricing, Lineup & How It Compares to Sling TV","excerpt":"As we head into 2026, Philo continues to position itself as an affordable streaming alternative for cable TV lovers. Discover what Philo offers, how its pricing stacks up against competitors like Sling TV, and what the Reddit community thinks about its future.","category":"technology"}],"metadata":{"metaTitle":"vLLM vs Triton: LLM Inference Comparison 2026","metaDescription":"vLLM vs Triton: Compare throughput, memory efficiency, and deployment complexity for LLM and multi-model inference servers.","publishedAt":"2026-07-09T12:32:14.660Z","updatedAt":"2026-07-09T12:32:15.283Z","isAutoGenerated":true,"isHumanReviewed":false,"viewCount":0}}