[Hardware Advice] On-Prem LLM for 30 Users: Single DGX Spark vs. Mac Studio (and the "2x Mac Mini" Dilemma)?

A 30-employee company seeking an on-premises AI platform for RAG and document analysis is weighing a single NVIDIA DGX Spark (GB10 Grace Blackwell, 128 GB unified memory, 273 GB/s bandwidth) against a single Apple Mac Studio (M-Series Max/Ultra, up to 192 GB memory, 800 GB/s bandwidth) or a budget path of two Mac Minis, citing strict data privacy requirements. The company prioritizes minimal upfront CapEx and asks the community for real-world benchmarks on single-node concurrency for 3–6 simultaneous requests, vLLM on CUDA versus Metal under load, and the viability of clustering two Mac Minis versus one Mac Studio.

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