{"slug": "hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the", "title": "[Hardware Advice] On-Prem LLM for 30 Users: Single DGX Spark vs. Mac Studio (and the \"2x Mac Mini\" Dilemma)?", "summary": "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.", "body_md": "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.\n\n**Active user base:** 30 seats (~3 to 6 concurrent requests during peak hours).\n\n**Target models:** 32B to 70B quantized models (Q4_K_M / FP8) or mid-sized MoEs.\n\n**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.\n\n**Hardware Contenders on the Table:**\n\n**Option 1: Single NVIDIA DGX Spark (GB10 Grace Blackwell)**\n\n*Specs:* 128 GB unified LPDDR5x (273 GB/s), ConnectX-7 (200 Gbps).\n\n*Pros:* Native CUDA ecosystem, production-grade serving via vLLM / TensorRT-LLM, continuous batching, PagedAttention.\n\n*Doubts:* Higher upfront cost per unit; memory bandwidth (273 GB/s) is lower than Apple Silicon Ultra chips.\n\n**Option 2: Single Apple Mac Studio (M-Series Max/Ultra)**\n\n*Specs:* 64 GB to 192 GB unified memory (up to 800 GB/s bandwidth on Ultra).\n\n*Pros:* High memory bandwidth (crucial for single-stream generation speed), power efficiency, silent, solid price-to-RAM ratio.\n\n*Doubts:* Concurrency handling under Metal/MLX/llama.cpp compared to vLLM on CUDA; limited production serving tooling for multi-tenant setups.\n\n**Option 3: The Budget Path (Mac Mini vs Mac Studio)**\n\n**Questions for the Community:**\n\n**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?\n\n**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?\n\n**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?\n\n**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?\n\nLooking for real-world benchmarks, war stories, or recommendations on the best cost-to-token sweet spot for a company of our size!", "url": "https://wpnews.pro/news/hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the", "canonical_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_at": "2026-09-07 11:19:56+00:00", "updated_at": "2026-09-07 11:25:44.741459+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-products", "ai-tools"], "entities": ["NVIDIA DGX Spark", "Apple Mac Studio", "Apple Mac Mini", "NVIDIA", "Apple"], "alternates": {"html": "https://wpnews.pro/news/hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the", "markdown": "https://wpnews.pro/news/hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the.md", "text": "https://wpnews.pro/news/hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the.txt", "jsonld": "https://wpnews.pro/news/hardware-advice-on-prem-llm-for-30-users-single-dgx-spark-vs-mac-studio-and-the.jsonld"}}