When 64GB of memory is not enough, the enterprise tier is where private AI infrastructure gets serious. This guide covers two paths to running 70B+ models at full precision, serving multiple users simultaneously, and building an AI server that handles production workloads without sending a single token to a third-party API.
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Option A: Dual RTX 5090 PC Build
Two RTX 5090s give you 64GB of combined VRAM with CUDA tensor parallelism. Components: ASUS TUF Gaming RTX 5090 32GB ×2 — $7,599.98 (Amazon). AMD Ryzen Threadripper PRO 7960X — $2,499.99 (Amazon). ASUS Pro WS TRX50-SAGE WIFI — $899.99 (Amazon). 256GB DDR5 ECC — $799.99 (Amazon). Samsung 990 Pro 4TB ×2 — $499.98 (Amazon). Seasonic PRIME TX-1600W — $449.99 (Amazon). Fractal Define 7 XL — $249.99 (Amazon). Noctua NH-U14S TR5-SP6 — $109.99 (Amazon). Total: ~$13,109 at current street prices.
Option B: Mac Studio M3 Ultra (192GB)
The Mac Studio M3 Ultra (Amazon) combines two M3 Max dies via UltraFusion, delivering 192GB unified memory at 819 GB/s. Price: $3,999 (96GB) or $5,999 (192GB). Power draw: 30W idle / 215W peak. Llama 3.3 70B at Q8 (~70GB) fits entirely in memory with 120GB to spare for KV cache. Annual electricity savings vs the dual RTX 5090 build: ~$1,200 at US average rates. Note: Apple released the 2025 Mac Studio with M4 Max (up to 128GB) and M3 Ultra (up to 192GB) — there is no M4 Ultra Mac Studio.
Head-to-Head: Enterprise Comparison
Dual RTX 5090 PC: ~$13,109 (street), 64GB VRAM + 256GB system RAM, Llama 70B decode ~35 tok/s, fine-tuning 13B ~4 hours, power draw ~1,200W, full CUDA, ~$1,400/year electricity. Mac Studio M3 Ultra: $5,999 (192GB), 192GB unified at 819 GB/s, Llama 70B decode ~22 tok/s, fine-tuning 13B ~18 hours, power draw ~215W peak, no CUDA, ~$190/year electricity.
Multi-User Inference Serving
For teams, vLLM (CUDA) and MLX Server (Apple Silicon) both expose OpenAI-compatible APIs. Combine with Tailscale for private network access and Authentik for access control.
# Dual RTX 5090: Serve Llama 3.3 70B with tensor parallelism (vLLM v0.18+)
vllm serve meta-llama/Llama-3.3-70B-Instruct \
--quantization fp8 \
--tensor-parallel-size 2 \
--max-model-len 32768 --port 8000
# Mac Studio Ultra: Serve Llama 3.3 70B at Q8 with MLX
python -m mlx_lm.server \
--model mlx-community/Llama-3.3-70B-Instruct-8bit \
--host 0.0.0.0 --port 8080Security for Enterprise Deployments
Place the AI server on a dedicated VLAN. Use Tailscale to create a private mesh network — team members connect via Tailscale and the server is never exposed to the public internet. Use HashiCorp Vault for secrets management and Authentik for SSO and RBAC on Open WebUI. For compliance-sensitive workloads (HIPAA, SOC 2), enable request logging in vLLM or Ollama to maintain an audit trail. RunPod offers HIPAA-compliant GPU instances as a cloud fallback when local capacity is exceeded.