Min VRAM needed
1.4 GB
Weights: 0.4 GB
KV cache: 0.4 GB
Overhead: 0.5 GB
At the $2,500–$3,000 price point, you have two genuinely compelling options for a private AI workstation: the Mac Studio M4 Max with 64GB unified memory, or a custom PC built around the RTX 5080 16GB. They are different machines with different strengths, and the right choice depends entirely on your workflow.
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Option A: Mac Studio M4 Max 64GB ($2,599)
The Mac Studio M4 Max 64GB (Amazon) delivers 64GB of unified memory at 546 GB/s bandwidth, 40 GPU cores with Neural Accelerators, and 22W idle / 92W peak power draw. The unified memory architecture means the entire model lives in fast memory — no VRAM/system RAM split. Llama 3.3 70B at Q2 (~18GB) runs entirely at full bandwidth. Power draw under inference is ~45W — the RTX 5080 PC draws 500W+ for the same workload. As an always-on private AI server, the Mac Studio is near-silent and designed to run 24/7.
Option B: RTX 5080 PC Build (~$2,966)
GPU: GIGABYTE WINDFORCE RTX 5080 16GB — $1,399.99 (Amazon). CPU: AMD Ryzen 9 9900X — $396.49 (Amazon). Motherboard: ASUS ROG Strix B850-F Gaming WiFi — $249.99 (Amazon). RAM: G.Skill Trident Z5 64GB DDR5-6000 — $179.99 (Amazon). Storage: Samsung 990 Pro 4TB NVMe — $249.99 (Amazon). PSU: Seasonic Focus GX 1000W — $209.99 (Amazon). Case: Fractal Design Torrent — $179.99 (Amazon). CPU Cooler: Noctua NH-D15 — $99.99 (Amazon). Total: ~$2,966.
Head-to-Head Comparison
Mac Studio M4 Max 64GB: $2,599, max model 70B at Q2, Llama 3.1 8B decode ~55 tok/s (MLX), fine-tuning 7B LoRA ~8 hours, power draw ~45W, no CUDA, excellent always-on server. RTX 5080 PC: ~$2,966, max model 32B at Q4 (16GB VRAM limit), Llama 3.1 8B decode ~95 tok/s (CUDA), fine-tuning 7B LoRA ~2 hours, power draw ~500W, full CUDA support, good server (noisier).
Running Models on Each Platform
On the Mac Studio, use MLX for maximum performance — it delivers 3–4x faster prefill than Ollama on the same hardware. On the RTX 5080 PC, vLLM provides the best inference throughput for production serving.
# Mac Studio: MLX for fast inference
pip install mlx-lm
python -m mlx_lm.generate \
--model mlx-community/Qwen3.5-27B-Instruct-4bit \
--prompt "Explain the CAP theorem"
# RTX 5080 PC: vLLM for production serving (v0.18+)
uv pip install vllm --torch-backend=auto
# New server command:
vllm serve Qwen/Qwen3.5-27B-Instruct-AWQ \
--quantization awq --max-model-len 32768