Guide hub
Guides for AI workload execution, GPU cost, and LLM deployment
Start here if you are working through workload routing, GPU cost, failover behavior, model fit, and the practical tradeoffs of running AI workloads across fragmented capacity.
Practical questions
Best for
Use these guides when you need operational answers, not marketing copy.
Cost, fit, failover
Coverage
The library focuses on the deployment questions teams hit most often.
Inference first
Strongest current angle
Inference remains the clearest proof point in the product today.
What you will find
Start with the practical questions teams ask first
These guides focus on the questions that come up once a team moves from experimenting with models to shipping them reliably. That means cost, fit, fallback behavior, and how much provider-specific logic you really want to own.
Use the guides to understand the problem first, then branch into model-specific pages or pricing when you want a more concrete route.
Related pages
Guide pages in this library
Choose the guide that matches the deployment or cost problem you are working through.
ai compute for beginner developersAI Compute for Beginner Developers: How to Start Without Buying GPUsAI compute for beginner developers is mostly about understanding the workload you want to run, then using cloud or routed GPU capacity instead of buying expensive hardware too early.what gpu do i need for aiWhat GPU Do I Need for My AI App? Start With the WorkloadThe right GPU for an AI app depends on the model, precision, latency target, and traffic pattern. Most teams should define the workload first instead of shopping hardware by brand name.what is ai inferenceWhat Is AI Inference? A Developer GuideAI inference is the moment a trained model takes new input and produces an output. For most developers shipping AI features, inference is the first real compute problem to understand.ai workload executionWhat Is AI Workload Orchestration?AI workload execution is the layer that decides where inference, training, and batch jobs should run based on fit, cost, latency, and reliability instead of forcing teams to choose raw GPU infrastructure by hand.run llm without gpu managementBest Way to Run LLMs Without Managing GPUsIf your team wants to ship open-source models without acting like a GPU broker, the winning pattern is to submit workload intent into an orchestration layer that handles provider choice, fit checks, and failover for you.reduce llm inference costHow to Reduce LLM Inference Cost Across GPU ProvidersReducing LLM inference cost is mostly a routing problem: matching the right model shape, precision, and demand pattern to healthy GPU capacity instead of buying more expensive headroom than the request needs.gpu failover for inferenceGPU Failover for Inference: What Happens When a Node DiesGPU failover matters because the cost of a bad node is not just a failed run. It is user-visible latency, retries, manual triage, and a stack of brittle provider-specific recovery playbooks.best gpu cloud for startupsBest GPU Cloud for Startups Running Open ModelsThe best GPU cloud for a startup is usually the stack that minimizes deployment drag and failed runs, not simply the vendor with the lowest headline rate on one GPU family.how to choose gpu for llm inferenceHow to Choose a GPU for LLM InferenceChoosing a GPU for LLM inference starts with the model, precision, concurrency target, and latency budget. Teams overspend when they shop by brand first and workload shape second.llm inference cost calculatorLLM Inference Cost Calculator: How to Estimate SpendA useful LLM inference cost calculator should incorporate fit, GPU price, runtime profile, concurrency assumptions, and retry risk. Hourly price alone is not a cost model.how to avoid gpu out of memory errorsHow to Avoid GPU Out-of-Memory Errors in InferenceGPU OOM errors in inference are usually a fit and deployment-policy problem. Teams can avoid them by sizing the model route correctly, using the right precision, and rejecting impossible placements before dispatch.best way to deploy open source llmsBest Way to Deploy Open-Source LLMs in ProductionThe best way to deploy open-source LLMs is to keep the developer workflow centered on workload intent while an execution layer handles fit, pricing, and provider choice underneath it.multi provider gpu orchestrationMulti-Provider GPU Orchestration for AI WorkloadsMulti-provider GPU orchestration matters when teams want flexible routing across fragmented supply without wiring provider-specific logic into every workload path.self host llm vs managed inferenceSelf-Hosted LLMs vs Managed InferenceThe self-hosted versus managed inference decision is really a question about how much routing, reliability, and GPU-operations work your team wants to own directly.