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Plan the system before choosing the parts.
Define what your local AI studio needs to do, which workloads must run together, and whether those workloads belong on your primary workstation, a dedicated AI server, or both.
START WITH THE WORKLOAD
What does your studio need to do?
Hardware requirements should follow the work—not the other way around. Begin by listing the models, creative applications, and automated tasks you expect the system to handle.
Language models and agents
Local chat, research, coding, document work, tool use, and multi-step agent workflows.
Image, video, and audio generation
ComfyUI workflows, image editing, animation, video generation, voice, transcription, and sound.
Blender and Unreal Engine
3D asset creation, scripting, scene management, rendering, MetaHuman workflows, and production assistance.
MCP and automation
Connections to files, applications, services, and repeatable workflows that may remain active in the background.
Training and model customization
Fine-tuning, LoRA training, embeddings, upscaling, or other compute-intensive work you may add later.
PLAN FOR REAL USE
What needs to run at the same time?
A system that can run each tool separately may still struggle when those tools share the GPU, memory, storage, and network. Plan around your heaviest realistic combination of tasks—not a single application running by itself.
Common simultaneous workloads include:
A local LLM or agent running while you work in Blender or Unreal Engine
ComfyUI generating images or video while an LLM coordinates the workflow
Multiple models loaded for text, vision, speech, or tool use
Rendering, model inference, and file processing sharing the same GPU and storage
Other computers accessing the AI system across your local network
CHOOSE THE SYSTEM APPROACH
Workstation, dedicated server, or both?
The right layout depends on whether AI workloads should share resources with your creative applications or run independently in the background. Each approach changes the cost, complexity, noise, power use, and upgrade path.
PRIMARY WORKSTATION
Run AI and creative applications on one powerful computer. This is the simplest and least expensive approach, but demanding AI workloads compete with Blender, Unreal Engine, rendering, and everyday work for the same resources.
DEDICATED AI SERVER
Move models and automation to a separate system accessed over your local network. This keeps the workstation responsive and allows services to remain available, but adds hardware cost, networking, power use, and maintenance.
HYBRID STUDIO
Use the workstation for interactive creative work and selected GPU-heavy tasks while a server handles persistent models, agents, automation, and shared services. This offers the most flexibility, but requires clearer workload planning.
CREATE YOUR SYSTEM BRIEF
Write down the requirements before comparing parts.
A short system brief prevents the build from drifting toward expensive hardware that does not support the way you actually plan to work. Record the following decisions before moving into the Hardware guide.
Primary outcomes: What do you want the studio to produce?
Core applications: Which AI and creative tools must it support?
Target models: Which language, image, video, audio, or specialized models do you expect to run?
Simultaneous workloads: What is the heaviest realistic combination of tasks?
System approach: Will you use a workstation, dedicated server, or hybrid setup?
Access: Will the system serve one computer, multiple devices, or remote users?
Constraints: What are your budget, space, power, noise, and upgrade limits?
NEXT STEP
Translate the plan into hardware requirements.
Use your system brief to determine the GPU, VRAM, CPU, memory, storage, networking, power, and cooling your local AI studio will need.