I Benchmarked vLLM, TensorRT LLM and Dynamo RTX6000, so You Don't Have To Shocking Results! скачать в хорошем качестве

I Benchmarked vLLM, TensorRT LLM and Dynamo RTX6000, so You Don't Have To Shocking Results!

Which enterprise inference engine actually delivers the best performance? I expanded my previous benchmark to include NVIDIA's TensorRT-LLM and Dynamo orchestration - testing 4 major inference engines on the same hardware with identical workloads.. 🔥 What You'll Learn: ✅ TensorRT-LLM vs vLLM: Performance comparison on identical hardware ✅ Dynamo orchestration layer: When distributed serving makes sense ✅ NATS + etcd architecture for production deployments ✅ Real benchmarks: 1000 requests across all 4 engines ✅ Docker setup: From simple single-engine to multi-service orchestration ✅ ShareGPT vs Random datasets: Which test matters for YOUR use case ✅ Production deployment complexity: Time vs performance tradeoffs 📊 Benchmark Battle Results: 🔧 Test Setup: Hardware: RTX 6000 PRO Blackwell (96GB VRAM) Drivers: CUDA 13.1 (590.48.01) Model: Qwen3-32B-FP8 Load: 1000 concurrent requests (burst + controlled) Datasets: ShareGPT (real conversations) + Random (uniform) Context: 10,000 max tokens Perfect for AI engineers, MLOps teams, and infrastructure architects evaluating production LLM deployment strategies. ⏱️ Timestamps: 0:00 Why Enterprise Inference Engines Matter 0:53 Testing 4 Engines: Overview 0:57 Dynamo: Data Center Scale Inference Framework 1:43 TensorRT-LLM: NVIDIA's Optimized Engine 2:06 Repository Setup & Environment Configuration 2:44 Docker Architecture Explained 3:18 Single Engine Deployment (TensorRT-LLM) 4:30 vLLM Deployment & Compatibility Issues 6:04 Dynamo Multi-Service Architecture Deep Dive 7:10 NATS Message Broker & etcd Configuration 8:37 Manual Dynamo Setup (Step-by-Step) 10:01 Local Mode vs Server Mode Comparison 11:35 Parameter Tuning Philosophy 12:44 ShareGPT vs Random Dataset Strategy 13:21 Running the Benchmarks 14:22 GPU Usage Analysis & Visualization 15:17 Results Analysis & Comparison 16:00 TensorRT-LLM Wins: Why It's Fastest 16:31 Concurrency Patterns Explained 17:39 Future Plans & AI Perf Tool 18:03 Practical LLM Comparison Guide 19:39 Wrap-up & Next Steps 📦 Resources: ✨ GitHub Repo: https://github.com/lukaLLM/AI_Inferen... 📚 Documentation: NVIDIA Dynamo: https://github.com/ai-dynamo/dynamo TensorRT-LLM: https://github.com/NVIDIA/TensorRT-LLM vLLM: https://github.com/vllm-project/vllm | https://docs.vllm.ai SGLang: https://github.com/sgl-project/sglang | https://docs.sglang.ai 🛠️ Requirements: CUDA 13.1+ drivers (590.48.01) Docker & NVIDIA Container Toolkit RTX 6000 PRO or L40S GPU (or similar with 40GB+ VRAM) Linux environment (tested on Ubuntu 24.04) Hugging Face account with access token Want more production LLM content? I cover async processing, cost optimization, and real-world deployment patterns! 👍 Like this video if you want more enterprise AI infrastructure content! 💬 Comment which engine you're using in production 🔔 Subscribe for practical AI engineering tutorials #TensorRTLLM #vLLM #SGLang #Dynamo #LLMInference #AIEngineering #NVIDIA #MLOps #RTX6000PRO #Blackwell #InferenceOptimization #EnterpriseAI #ProductionML #GPUOptimization #AIInfrastructure #ModelServing #DockerDeployment #DistributedSystems #AIBenchmarking #MachineLearning