Hardware / AI Workstations
Built around the model.
GPU and VRAM sized for local language models, image generation, video, data work, and development—with the CPU, memory, cooling, and power to match.
AI systems and automation
Gamecraft IT Solution helps plan AI-ready computers, local model software and practical automation around a defined task. We consider the hardware, tools, data access and review process together before selecting an approach.
Discuss your project
Software / Local AI
Private AI, close to your data.
Tools such as Ollama, LM Studio, or AnythingLLM configured for local chat, document search, development, and controlled experimentation.
Programs / Workflows
From install to outcome.
Model runtimes, Python environments, accelerated libraries, APIs, containers, and automations configured around a useful business task.
System Design / What Matters
Performance is a complete system.
A large GPU alone does not make a balanced AI computer.
- GPU & VRAMSelected for model size, generation speed, resolution, and concurrent workloads.
- CPU & memoryBalanced for data preparation, development tools, and feeding the accelerator efficiently.
- StorageFast, scalable capacity for models, datasets, caches, projects, and backups.
- Thermals & powerCooling, case airflow, and power delivery designed for sustained workloads.
- Software pathLocal first where it fits; cloud or hosted models when scale and uptime call for it.
AI systems and automation
Gamecraft IT Solution helps plan AI-ready computers, local model software and practical automation around a defined task. We consider the hardware, tools, data access and review process together before selecting an approach.
- Who it is for
- Businesses, developers and creators exploring local AI, document tools or repeatable workflow automation.
- Where we work
- AI system planning and local services in Melbourne. Remote work, deployment and support requirements are agreed for each project.
What we can help with
- AI workstations
- GPU memory, RAM, storage and cooling considered against the model size and intended workload.
- Local model tools
- Review model runtimes, document tools and setup requirements for the chosen hardware and use case.
- Workflow automation
- Start with a repeatable business task, define inputs and expected outputs, and include an appropriate review or approval step.
- Access and operating costs
- Agree data permissions, software licences, any cloud or API usage and maintenance responsibilities before deployment.
How a project starts
Choose a useful task
Describe the process, current tools, data and the outcome you want to improve.
Review the approach
Compare local and connected tools, hardware requirements, access controls and expected operating costs.
Validate a small workflow
Review representative outputs and exceptions before deciding whether to expand the system.
Questions before you start
Discuss your projectCan AI run locally without a paid API?
Some models and tools can run locally without per-request API charges. Hardware, software licensing, electricity and maintenance still matter, and suitability depends on the task and model.
Does local AI automatically keep every document private?
No. Privacy depends on the complete setup, including plugins, connected services, telemetry, access permissions and backups. These need to be reviewed for the chosen tools.
Can AI automate every part of a business task?
Not reliably in every case. We start with a defined task and representative examples, then consider exceptions and where human review is necessary.
What do you need to scope an AI project?
Tell us the task, current software, data sensitivity, expected users and budget. This helps determine whether local tools, cloud services or a simpler non-AI workflow fits.
The right machine is only the start. We make the system productive.
Plan an AI system