Serverless GPU vs Dedicated GPU: Choosing the Right Model
Choose between managed serverless inference and a dedicated GPU instance by workload persistence, customization, access, and cost controls.
Hostnot GPU engineering
GPU, AI, and infrastructure guides
Practical notes for choosing hardware, connecting safely, and building dependable AI workloads. Every guide reflects what the platform actually does today.
Showing 19–36 of 45 published articles
Serverless GPU Inference: Getting Started
A safe first-request workflow for discovering a model, storing an API key, sending a request, and interpreting errors.
GPU Driver Updates on Linux: Safe Update Procedure
A cautious driver-update workflow with compatibility checks, rollback planning, and post-update verification.
API Rate Limiting Strategies for GPU Inference
Protect inference services with bounded concurrency, per-customer budgets, backpressure, and retry-safe request handling.
Open-Source LLM Models: Size, Capabilities, and GPU Matching
A practical model-selection framework covering license, capability, memory, context, serving support, and evaluation.
Mistral 7B Local Inference: GPU Requirements
Plan a Mistral 7B deployment by checkpoint, precision, context, KV cache, runtime support, and test workload.
LLM Inference Quantization: 4-bit and 8-bit Guide
Understand weight quantization, formats, accuracy tradeoffs, KV cache memory, and how to validate a quantized deployment.
GPU Benchmarking Basics: Synthetic and Workload Benchmarks
Design repeatable GPU benchmarks that separate hardware capability from model, framework, input, and operational effects.
GPU Cloud Comparison: Capacity, Control, and Workload Fit
Compare GPU cloud providers by workload fit, region, networking, storage, operations, and transparent pricing rather than brand alone.
GPU Temperature and Power Limits: Best Practices
Measure thermal and power behavior safely and understand why power-limit changes are not a universal performance fix.
Stable Diffusion WebUI: GPU Optimization and Settings
A practical image-generation setup covering VRAM, precision, attention backends, resolution, and safe remote access.
LLaMA 2 Local Inference (Historical): Memory and Runtime Planning
A historical, reproducible planning guide for Llama 2 inference, with explicit license, memory, context, and runtime caveats.
Multi-GPU Setup with PyTorch: Data Parallel and Distributed
Understand when to use one process per GPU, how to launch distributed jobs, and how to verify that work is actually parallelized.
Docker GPU Setup: NVIDIA Container Runtime
Install and verify GPU access in Docker while keeping host drivers, images, permissions, and secrets under control.
GPU Troubleshooting: Blue Screen and Crash Recovery
A safe incident checklist for host crashes, kernel faults, workload failures, and preserving diagnostics.
OpenAI-Compatible APIs for Self-Hosted and Open Models
Compatibility is an interface contract, not a guarantee that every model, feature, or streaming behavior is available.
GPU Cloud Pricing Explained: What Determines Hourly Cost?
Understand the variables behind a customer-facing GPU rate and why static comparison tables quickly become stale.
A100 40GB vs A100 80GB: What's the Difference?
Why the memory capacity in an A100 configuration changes model fit, context headroom, and concurrency planning.