Guides

Learn why VRAM matters for local LLMs. Stratat uses RAM for large models. See how to check your hardware and optimize your Stratat client setup.

Compare AMD RX 7900 XTX and RX 6900 XT for local inference. We analyze VRAM, bandwidth, and cost-per-token to determine the best value for Stratat.

Analyze RTX 4090 performance with IQ2-XS quantization. Review VRAM usage, throughput, power draw, and running costs for local AI models.

Discover how the RTX 4090 handles iQ3-S quantization with 24GB VRAM. We analyze 4000 TPS reading speed and 550W power draw for local AI inference.

Analyze the RTX 4090's capacity for the coder quantization. Review VRAM fit, token throughput, and power draw to determine if this setup fits your local AI workflow.

Explore how Comcast bandwidth caps and latency affect running large AI models locally with Stratat. Learn to optimize your connection for better inference speeds.

Navigate dorm network limits and power caps for local AI. Use Stratat to run models offline, respecting school rules and electrical safety without external dependencies.

Learn how to calculate the real kWh cost of running local AI models in your apartment. Use Strata to optimize your power budget and avoid bill shocks.

Learn to batch-process large datasets using Stratat's local engine. This guide covers setup, cost analysis using $0.17/kWh electricity rates, and optimization for privacy.

Learn to calculate the true cost of running large language models locally with Stratat. Compare energy usage against cloud API pricing to decide.

Learn how to back up your Stratat engine models locally. Preserve your data, ensure privacy, and maintain offline access without relying on cloud services.

Resolve the context-truncated error in Stratat by managing context windows and optimizing input sizes to prevent local AI model failures.

Resolve amd-fallback errors in Stratat by verifying local GPU availability and model compatibility. Step-by-step diagnostic guide for Windows and macOS users.