Why run an LLM locally?

Open-source language models (Llama 3.3, Qwen 3, DeepSeek V3, Mistral) now match GPT-4o quality on most tasks. Three solid reasons to run them locally instead of through an API:

1. Absolute privacy. Your legal documents, medical records, HR data or proprietary code never leave your infrastructure. This is non-negotiable for law firms, clinics, accounting offices and businesses bound by Quebec's Law 25 or Canada's PIPEDA.

2. Predictable cost. Once the machine is amortized (12-24 months depending on usage), your marginal cost per query drops to zero. A team of 10 people running 200 LLM calls per day saves $8,000-15,000 CAD/year versus a premium cloud API.

3. Local latency. No internet dependency, no rate limits, no performance fluctuations based on the provider's server load.

Criterion #1: VRAM, not raw speed

For running an LLM, what matters first is VRAM capacity. The pragmatic rule for Q4 quantization (~99% quality of the full version):

A faster GPU with less VRAM simply won't load the model. Throughput comes second.

Recommended configurations for 2026

Tier 1 — Discovery (entry-level pro, $500-1,200 CAD)

RTX 5060 Ti 16 GB GDDR7 — Sweet spot price/performance for 2026. Comfortably runs 7-13B models with long context. Ideal for: solo developers, evaluation testing, first team RAG server.

Typical tokens/s: 80-100 tok/s on Qwen 3 8B, 35-50 tok/s on 13B.

Tier 2 — Light production ($1,100-2,400 CAD)

RTX 5070 12 GB GDDR7 — Superior speed for compact models but limited to 12 GB.

RTX 5080 16 GB GDDR7 — Best compromise if you stay on 7-30B. Top speed, 16 GB sufficient for 30B in Q4. Tokens/s: 50-60 on Qwen 3 32B.

Tier 3 — Serious production ($5,500-7,000 CAD)

NVIDIA RTX PRO 5000 48 GB GDDR7 — The overlooked sweet spot of the Canadian market. 48 GB lets you load Llama 3.3 70B in Q4 with comfortable headroom for long context (32K-128K tokens). 1.8 TB/s memory bandwidth — nearly identical to RTX 5090.

Why RTX PRO instead of 5090? Three reasons:

• 48 GB vs 32 GB: you fit a 70B, not just a 32B
• Studio/Enterprise drivers stable for production
• Canadian OEM support for 3 years

This is our main recommendation for SMBs that want a single machine that covers all use cases.

Tier 4 — Multi-GPU / local cluster ($10,000-25,000+ CAD)

Beyond this, you move to dual GPU or Threadripper PRO platforms. AMD Ryzen Threadripper PRO 7965WX 24-core opens 128 PCIe 5.0 lanes — you can mount 2-4 GPUs without bottleneck.

Use cases: fine-tuning, multi-tenant servers (teams of 20+), in-house model training.

What about NVIDIA DGX Spark?

Launched October 2025 at $3,999 USD then raised to $4,699 USD in February 2026 due to LPDDR5x memory shortages, the DGX Spark is a very interesting alternative: unified mini-PC with 128 GB of shared GPU/CPU memory, capable of running up to 200B parameters.

Pros: Minimal footprint, low power draw, pre-installed NVIDIA stack. Cons: Lower memory bandwidth than RTX (so lower tokens/s), less expandable.

For client demos, prototyping, branch office deployments — unbeatable. For a primary workstation, RTX PRO 5000 remains more versatile.

Quick decision table

• You're solo, you're testing: RTX 5060 Ti 16 GB
• You're setting up team RAG (5-20 people): RTX 5080 or RTX PRO 5000
• You want comfortable 70B locally for pro practice: RTX PRO 5000 + Threadripper PRO
• You want a plug-and-play appliance: DGX Spark
• You're building a training cluster: Threadripper PRO platform + 2-4 GPUs

Concretely in Canada

PcHybrid keeps in stock locally (CAD pricing, GST/QST included, Canadian OEM warranty):

• RTX PRO 5000 48 GB
• RTX 5080 / 5070 / 5060 Ti in multiple brands (ASUS TUF, Prime, Dual)
• AMD Threadripper PRO 7965WX
• RTX A400 / A1000 / A2000 pro cards for workstation upgrades

See our full collection: Local LLM AI Workstations

Conclusion

The 2026 sweet spot for most Canadian use cases is the RTX PRO 5000 48 GB. It's the only GPU combining sufficient VRAM for Llama 3.3 70B, stable enterprise drivers, and a price that stays under $7,000 CAD. Paired with a Threadripper PRO for future scalability, it's a platform that will last 4-5 years before needing an upgrade.

If your budget is tighter, drop down to the RTX 5080 16 GB while accepting the 30B parameter ceiling. That's amply sufficient for 80% of SMB use cases (RAG, classification, assisted generation).

Article written May 2026. Prices and availability evolve rapidly on this segment — check the product page for current conditions.