Everyone keeps saying,
“just run AI locally.”
Let’s put some numbers against that.
If you want to run a decent local model – not toy models, but something in the 14B to 70B range – you are stepping into real infrastructure territory.
Here’s what that actually looks like today.
Option 1 – NVIDIA GPU (performance-first)
A single NVIDIA GeForce RTX 3090 or NVIDIA GeForce RTX 4090 is the practical baseline.
- ~$1,000 – $3,000 CAD for the GPU (used → new, depending on availability)
- Add a proper system (CPU, motherboard, PSU, cooling, case)
- Add 64GB+ RAM
Realistically:
$5K – $8K CAD all-in
What you get:
- Fast inference
- 14B–30B models comfortably
- 70B possible with quantization and trade-offs (often slower, may require CPU offload)
What you don’t get:
- simplicity
- low power usage
- quiet operation
This is a workstation, not a casual setup.
Option 2 – Apple Silicon (memory-first)
Something like a Apple Mac Studio M2 or Apple MacBook Pro 16-inch with 64GB unified memory.
- ~$4,000 – $5,500 CAD depending on configuration
What you get:
- larger models fit more easily due to unified memory
- lower power draw
- quieter, stable 24/7 operation
What you don’t get:
- raw speed vs NVIDIA GPUs
- upgrade flexibility
This is closer to an appliance than a build.
Option 3 – Prebuilt “AI-ready” tower
Vendors bundle systems with a 4090, but:
- often ship with 16–32GB RAM (not enough)
- require upgrades to reach 64GB+
Expect:
$5K+ CAD after upgrades
What people miss
The hardware is just the entry fee.
You are also taking on:
- thermal management (these systems run hot under sustained load)
- power consumption (high-end GPUs can draw ~400W+)
- model management (quantization, VRAM constraints, offloading)
- orchestration (agents, queues, workloads)
- ongoing tuning and supervision
This is not “install and go.”
The real trade-off
Cloud AI:
- pay per use
- near-zero operational overhead
Local AI:
- upfront capital cost
- full control
- ongoing operational responsibility
So the real question is not:
“Can I run AI locally?”
It’s:
“Do I want to operate an AI system?”
Bottom line
Running local AI today is closer to:
owning a small compute cluster
…than installing an app.
That may be exactly what you want.
But it is not free, and it is not trivial.
If you are considering this, start here:
- What outcome do I need?
- What latency or privacy constraints matter?
- How much operational complexity am I willing to absorb?
Everything else flows from that.
StayFrosty!
Q&A Summary:
Q: What is the cost range for setting up an NVIDIA GPU-based AI system locally?
A: The cost ranges from $5,000 to $8,000 CAD for a complete system including the GPU, CPU, motherboard, PSU, cooling, case, and 64GB+ RAM.
Q: What are the benefits of using Apple Silicon like the Mac Studio M2 for running AI locally?
A: Apple Silicon offers the advantages of fitting larger models due to unified memory, lower power consumption, and quieter, stable operation 24/7.
Q: What are the limitations of running AI on Apple Silicon compared to NVIDIA GPUs?
A: Apple Silicon generally has lower raw speed compared to NVIDIA GPUs and lacks upgrade flexibility.
Q: What additional responsibilities come with running AI locally on your own hardware?
A: Local AI operations involve thermal management, high power consumption, model management including quantization and VRAM constraints, orchestration of agents and workloads, and ongoing system tuning and supervision.
Q: What are the key differences between using cloud AI services and running AI locally on your own hardware?
A: Cloud AI services offer pay-per-use with minimal operational overhead, while local AI requires significant upfront capital cost, full control over the system, and extensive ongoing operational responsibility.
