Build planning

Local AI workstation build planning pages

Draft planning routes for local LLM, image workflow, and high-VRAM workstation decisions. These pages help organize questions before hardware evidence, compatibility checks, and benchmark sources are attached.

Build pages are planning routes only. Verify VRAM needs, exact GPU variants, component compatibility, power, cooling, runtime support, and benchmark evidence before local hardware decisions.

Choose a planning route

Starter buildFirst local LLM experiments with calculator-first validation.16GB VRAM buildMid-range local AI planning where headroom may be borderline.High-VRAM workstationHeavier local workloads with power, cooling, and stability checks.Image workflow buildCreator and image-generation planning with storage/cache checks.Cloud vs LocalValidate cloud testing before local hardware commitment.

Build planning routes

Planning confidence: Needs verification

Local LLM Starter Build Planning

Starter local LLM build planning guide for choosing a GPU-first component path, checking CPU/RAM/storage roles, and avoiding compatibility traps.

Use case: First local LLM experiments and private assistant testing
VRAM tier: 12GB to 16GB planning tier
Key build constraints: RAM/headroom, model size, runtime stack, calculator-first validation
GPU planning class: 12GB to 16GB source-backed GPU planning profiles

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Planning confidence: Needs verification

Local AI 16GB VRAM Build Planning

16GB VRAM local AI planning guide for deciding whether the workload has enough headroom before comparing GPU candidates.

Use case: Broader local AI experiments where 8GB to 12GB may feel constrained
VRAM tier: 16GB planning tier
Key build constraints: VRAM headroom, runtime overhead, future model growth, cloud validation
GPU planning class: 16GB GPU profiles with source-backed VRAM fields

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Planning confidence: Needs verification

High-VRAM Local AI Workstation Planning

24GB+ local AI workstation planning guide for deciding when high VRAM is useful, when to test cloud first, and what system constraints can break the plan.

Use case: Heavier local AI experiments, larger model research, and workstation validation
VRAM tier: 24GB+ planning tier
Key build constraints: PSU/cooling, connector checks, case clearance, stability evidence
GPU planning class: 24GB and larger high-VRAM GPU planning profiles

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Planning confidence: Needs verification

Image Workflow AI Build Planning

Planning route for image-generation and creator workflows where VRAM, runtime, and extensions can change requirements.

Use case: Image generation, creator workflows, and extension-heavy local AI experiments
VRAM tier: 16GB to 24GB planning tier
Key build constraints: Storage/cache, generated outputs, runtime extensions, driver caveats
GPU planning class: 16GB to 24GB GPU profiles for image workflow planning

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Planning confidence: Needs verification

Cloud GPU vs Local AI Build Planning

Decision framework for testing cloud GPU first, planning local AI hardware, or using a hybrid validation path before commitment.

Use case: Users deciding whether local hardware is worth validating before commitment
VRAM tier: Variable VRAM planning tier
Key build constraints: Cloud testing, workload frequency, local control needs, hardware risk
GPU planning class: Compare local GPU tiers against a cloud test-first workflow

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How to use these build pages

01

Estimate memory first

Use the calculator to frame VRAM needs before comparing GPU names or workstation routes.

02

Review GPU planning profiles

Check source-backed GPU specs where available and keep draft fields marked for verification.

03

Compare close options

Use comparison pages for capacity, memory, power, and runtime caveats without benchmark claims.

04

Verify the exact build

Confirm part compatibility, power, cooling, OS, drivers, and workload evidence before hardware decisions.

NEXT STEP

Start with memory needs before comparing hardware paths

Use the VRAM Calculator to frame capacity needs before comparing GPU profiles, comparison pages, and build planning notes.

Estimate VRAM first