Blog & Guides

Everything you need to know about deploying Llama 4 Maverick and Scout models. Learn about quantization strategies, VRAM requirements, and performance optimization tips.

DeepSeek R1 is a 671B parameter model, but can you run it on consumer hardware? We explore quantization options, distilled variants, and real-world performance.

NVIDIA's RTX 5090 brings 32GB of VRAM and Blackwell architecture. We test its AI inference capabilities against the RTX 4090.

Learn Ollama from scratch, quickly deploy Llama, DeepSeek and other mainstream models locally. Includes installation, configuration, common commands and best practices.

Deep comparison of two flagship consumer graphics cards in AI inference tasks, including speed, VRAM, power consumption and cost-effectiveness analysis.

Complete guide to vLLM inference engine installation, configuration, and production deployment best practices. Learn PagedAttention principles, Continuous Batching mechanisms, and how to build enterprise-grade LLM inference services supporting thousands of concurrent requests. Includes Docker deployment, multi-GPU configuration, and monitoring solutions.

Detailed ExplanationDeepSeek R1DistillSystemColumnEachVersionFeatures, help you based onHardwareConfigurationSelectMostSuitable forModelVersion.

Deep dive into Tensor Parallelism core principles and implementation mechanisms. Learn how to efficiently deploy 70B, 180B, and larger language models in multi-GPU environments. Includes practical vLLM and llama.cpp configurations, performance optimization techniques, and troubleshooting guides.