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

> Source: <https://discuss.huggingface.co/t/hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the-2x-mac-mini-dilemma/180022#post_1>
> Published: 2026-09-07 11:19:56+00:00

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!