For a small company, mostly yes, with clear limits. A Mac Studio M5 Ultra with 256 GB or 512 GB can hold a large language model, an embedding model for document search and several agents in memory at once, and it draws little power. Where it falls short is serving many people at the same moment. The batching software that lets one machine answer dozens of requests in parallel is newer and less proven on macOS than NVIDIA's vLLM stack, and published Mac results show modest scaling. It's a reasonable fit for an office of a few to a few dozen people who rarely send requests at the same instant. It isn't a replacement for a GPU server behind a busy customer-facing service.
How well does a Mac Studio serve many users at once?
| System and test | Model and software | 1 request | More requests at once | Source |
|---|---|---|---|---|
| Mac Studio M5 Ultra 256 GB | Qwen3.8-Flash-Next (MoE), oMLX, ~6.5K-token prompts | 66.2 tokens/s total | 69.6 at 2 requests; 81.5 at 3 | MacStories (Sep 2026) |
| Mac Studio M5 Ultra 256 GB | gpt-oss-120b, oMLX | 110 tokens/s | 180.6 at batch 2; 280.8 at batch 4 | oMLX community result (Sep 2026), unverified |
| Apple M4 Max | vllm-mlx (academic project) | — | 4.3x aggregate throughput at 16 concurrent requests | arXiv paper (Jan 2026) |
| NVIDIA GB10 (DGX Spark class) | gpt-oss-120b, llama.cpp, 512-token prompt | 57 tokens/s | 159 at batch 8; 353 at batch 32 | Community llama.cpp benchmarks |
| NVIDIA GB10 (Dell Pro Max) | gpt-oss-120b, vLLM | 69 tokens/s | Up to 671 at batch 64 | StorageReview (Mar 2026) |
| NVIDIA RTX PRO 6000 Blackwell | gpt-oss-120b MXFP4, 512 in / 512 out | — | 3,064 tokens/s at concurrency 128 | StorageReview (2026) |
Source: as listed in each row; all figures are total output tokens per second across requests. Engines, prompts and settings differ, so treat rows as indications of how each system scales, not like-for-like results. SourceIT has not run these tests.
What does a company-wide chatbot actually need?
An internal chatbot has three demands: a model good enough to be useful, enough memory for each conversation's context (the KV cache), and software that can interleave requests from several people. The Mac Studio handles the first two well. A 256 GB M5 Ultra gives the GPU about 192 GB by default, enough for a 120B-class model plus generous context for several users.
The third demand is where the choice of software matters most:
- vllm-metal, the official Metal plugin for vLLM, had its first release on 22 Sep 2026. It brings continuous batching, a paged KV cache and prefix caching to the Mac, and an OpenAI-compatible API that chat front ends can connect to. It supports fewer model families than CUDA vLLM.
- llama.cpp and LM Studio's llama.cpp engine can serve parallel requests (LM Studio defaults to 4 slots). As of LM Studio 0.4.0, its MLX engine didn't support request batching.
- oMLX and vllm-mlx are community projects that show better scaling, but they are young.
How many people can that serve? It depends on how many requests arrive at the same moment, not on headcount. As an illustration only: if a 20-person office has three or four requests in flight at peak, the oMLX figures above would give each user roughly 70 tokens per second (280.8 ÷ 4) on gpt-oss-120b, which is comfortable reading speed. If those figures don't hold, MacStories' weaker scaling (81.5 tokens per second shared by three requests, or about 27 each) is still usable. Our GB10 guide to 20 concurrent users works through the same question for NVIDIA's platform, and building a corporate ChatGPT on GB10 covers the software set-up.
Can the same Mac also handle document search (RAG)?
Retrieval-augmented generation (RAG) lets the chatbot answer from your own documents. It needs an embedding model to index them, a vector database, and the main language model, all running together. Unified memory makes this easier on a Mac: with 256 GB, all three fit with room to spare. For comparison, NVIDIA's multi-agent chatbot playbook for the GB10 uses about 120 GB of its 128 GB.
Two cautions apply. First, embeddings are still marked experimental in vllm-metal, so many teams will run the embedding model in a separate tool. Second, RAG sends long prompts, often thousands of tokens of retrieved text per question. That stresses prompt processing, where NVIDIA's discrete GPUs lead clearly: with a 63K-token prompt, MacStories measured 42.8 seconds to the first token on the M5 Ultra against 26.4 seconds on an RTX 5090. For comparison, one published deployment of NVIDIA's RAG Blueprint on a GB10 was estimated to serve about 10 users.
One Mac Studio can hold a whole company's AI in memory. Whether it can answer everyone at once depends on how many ask at the same moment.
What about AI agents running all day?
Agents that triage email, draft reports or watch systems run unattended for hours. The Mac Studio's power profile suits this. Apple rates the M5 Ultra at 9 W at idle, which is about S$25 a year at SP Group's 31.16 cents per kWh including GST. Even a constant 300 W, the peak Apple's researchers measured during LLM inference, would be about S$819 a year (illustrative calculation: 0.3 kW × 8,760 hours × S$0.3116).
Agents also resend long contexts on each step, so prompt processing speed and prefix caching matter more than raw generation speed. Our post on local AI agents on your desk covers agent design, and our Mac Studio versus GB10 comparison for 24/7 agents compares reliability and energy use directly.
What breaks when one machine runs everything?
The bigger risks are operational, not performance:
- A single point of failure. If the Mac is down for an update or repair, every user and agent stops. The standard warranty in Singapore is one year, extendable to three with AppleCare+.
- Restarts need planning. Automatic power-on after an outage needs macOS 26.5 or later. FileVault disk encryption has no supported way to unlock unattended after a reboot, and macOS updates can't be run over SSH.
- No GPU in containers. Docker on macOS can't pass the GPU through, so the model server runs directly on macOS rather than in a container.
- Fleet management. Apple Business, launched in April 2026, includes free device management, but Singapore availability wasn't confirmed in the sources we found.
- Power protection. A UPS keeps short outages from corrupting work in progress.
Some companies split the load across two machines instead. Our post comparing one Mac Studio with two RTX Spark PCs covers that option.
When should a company choose NVIDIA instead?
NVIDIA hardware is the safer choice when many people use the system at once, when work must move to NVIDIA servers, or when the team fine-tunes models. The vLLM results above show why: a GB10 reached 671 tokens per second at batch 64, and an RTX PRO 6000 reached 3,064 at concurrency 128. A Mac Studio is the stronger choice when the largest model matters more than peak concurrency, or when low power and simple set-up count for more than CUDA. Our CUDA versus MLX post covers the software side, and our local AI versus cloud API cost model helps size the budget.
Buying in Singapore
SourceIT supplies both routes for company AI. The Mac Studio M5 Ultra with 256 GB and the M5 Ultra with 512 GB are price on request; for reference, the 256 GB/2 TB model is about S$16,699 configured and the 512 GB/4 TB model about S$35,449 (both calculated from Apple Singapore's upgrade pricing — confirm on apple.com/sg). For NVIDIA's platform, the NVIDIA DGX Spark 4TB is S$9,300, the HP ZGX Nano 4TB is S$11,499 and the RTX PRO 6000 Blackwell Workstation Edition is S$29,100 (listed 8 Nov 2026). For power protection, the APC Back-UPS Pro 1500VA is S$886.25. Prices change; ask for a current quotation. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
How many users can one Mac Studio serve? It depends on how many requests arrive at once, not on headcount. Published M5 Ultra tests show total throughput rising from about 66 to 82 tokens per second at three requests in one test, and up to about 281 at four in an unverified community test. That suits a small office better than a busy public service.
Can a Mac Studio run vLLM for a company chatbot? Yes, through the vllm-metal plugin, released on 22 Sep 2026 with continuous batching and an OpenAI-compatible API. It's new and supports fewer models than CUDA vLLM, so test your chosen model before relying on it.
Is 256 GB or 512 GB better for company use? 256 GB fits a 120B-class model, an embedding model and multi-user context. 512 GB makes sense if you need much larger models, but it doesn't increase speed, which is set by the same 1.2 TB/s bandwidth.
Can a Mac Studio run unattended after a power cut? It can power on automatically on macOS 26.5 or later. If FileVault is enabled, the disk needs to be unlocked after a restart, which has no supported unattended method, so plan for that.
Should a company buy one large Mac or several smaller machines? One large Mac is simpler and holds the biggest models. Several machines avoid a single point of failure and can split users across them, at the cost of more administration.
