We are planning an on-premises deployment of a private, internal AI platform (RAG, document analysis, internal reasoning workflows) for a 30-employee company. Due to strict data privacy requirements, all data must remain strictly on-prem.
Active user base: 30 seats (~3 to 6 concurrent requests during peak hours).
Target models: 32B to 70B quantized models (Q4_K_M / FP8) or mid-sized MoEs.
Strategy: Start with a single machine to validate ROI and keep upfront CapEx minimal, with a clear path to scale only if concurrency becomes a bottleneck.
Hardware Contenders on the Table:
Option 1: Single NVIDIA DGX Spark (GB10 Grace Blackwell)
Specs: 128 GB unified LPDDR5x (273 GB/s), ConnectX-7 (200 Gbps). Pros: Native CUDA ecosystem, production-grade serving via vLLM / TensorRT-LLM, continuous batching, PagedAttention.
Doubts: Higher upfront cost per unit; memory bandwidth (273 GB/s) is lower than Apple Silicon Ultra chips.
Option 2: Single Apple Mac Studio (M-Series Max/Ultra) Specs: 64 GB to 192 GB unified memory (up to 800 GB/s bandwidth on Ultra).
Pros: High memory bandwidth (crucial for single-stream generation speed), power efficiency, silent, solid price-to-RAM ratio.
Doubts: Concurrency handling under Metal/MLX/llama.cpp compared to vLLM on CUDA; limited production serving tooling for multi-tenant setups.
Option 3: The Budget Path (Mac Mini vs Mac Studio) Questions for the Community:
Single-Node Viability for 30 Seats: For an office of 30 people (with occasional simultaneous queries), can a single machine (DGX Spark or Mac Studio) comfortably handle continuous batching without latency falling off a cliff?
vLLM (CUDA) vs. Metal (Apple Silicon) under Concurrency: Does vLLM’s continuous batching and KV-cache management on the DGX Spark give it an insurmountable lead over Apple Silicon when 4–5 users query at the same time?
1x Mac Studio vs. 2x Mac Mini: Has anyone tested clustering two Mac Minis (via 10GbE / Thunderbolt using Exo or llama.cpp RPC) versus running a single Mac Studio? Does network latency negate the cost savings of two smaller nodes?
Day-2 Scaling: If we start with 1 unit (either DGX Spark or Mac Studio), how seamless is adding a second identical unit down the line for distributed inference/pipeline parallelism?
Looking for real-world benchmarks, war stories, or recommendations on the best cost-to-token sweet spot for a company of our size!