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Short answer: choose the Mac Studio with M4 Max when your priority is the fastest tested single-box token generation and a mature Metal/MLX software stack. Choose the MINISFORUM MS-S1 MAX with 128GB of memory when fitting larger quantized models, using Windows or Linux, or adding a discrete GPU is more important than peak decode speed. Choose the MINISFORUM MS-01 for 10GbE networking, multiple NVMe drives, virtualization and homelab services; it needs a separate accelerator if local LLM inference is its main job.
Those conclusions come from different kinds of evidence. Tom’s Hardware’s 2026 llama.cpp tests favor the M4 Max in the workloads it measured, while MINISFORUM’s specifications and vendor demonstrations emphasize the MS-S1 MAX’s 128GB unified memory and PCIe expansion. The machines are therefore not interchangeable: one is a fast, tightly integrated inference appliance, one is a high-capacity and expandable AI workstation, and one is primarily a networked storage and server platform.
What this comparison can—and cannot—tell you
Local inference performance depends on the model, quantization, context length, backend, driver and memory pressure. A system that wins on a 35-billion-parameter mixture-of-experts model may not win on a dense model, and a published memory-bandwidth figure does not by itself predict delivered tokens per second. The most useful comparison is therefore a set of decisions rather than a universal ranking.
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- Decode speed: how quickly an already-loaded model produces output tokens.
- Prompt processing: how quickly the system ingests a prompt or document before generation.
- Model capacity: whether the model and its runtime state fit in available unified memory or VRAM.
- Software fit: how well Ollama, llama.cpp, MLX, Vulkan, ROCm or CUDA support your target model.
- Expansion and service role: whether you need a discrete GPU, more storage, fast networking or virtual machines.
The independent figures cited here are not a complete benchmark suite. Tom’s Hardware tested specific llama.cpp builds and model quantizations; MINISFORUM’s cluster results use multiple machines and cannot be treated as the speed of one MS-S1 MAX.
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At a glance: the three compact systems
| System | Core hardware | Memory or storage position | Best fit | Main limitation for inference |
|---|---|---|---|---|
| MINISFORUM MS-S1 MAX | AMD Ryzen AI Max+ 395; 16 cores/32 threads; integrated RDNA 3.5 graphics and NPU | Up to 128GB LPDDR5x-8000 quad-channel memory; PCIe expansion and additional storage | Large quantized models, Windows/Linux flexibility and future GPU expansion | Decode speed and backend support vary by model and driver; major CPU, GPU and RAM components are soldered |
| Mac Studio M4 Max | Apple silicon with a 40-core GPU in the tested configuration | Apple unified-memory configurations; the tested system is rated at 546GB/s memory bandwidth | Fast single-box generation, macOS, Metal and MLX workflows | Memory is fixed at purchase; no internal user-upgrade path or PCIe slot |
| MINISFORUM MS-01 | Intel Core i9-13900H or i9-12900H; integrated Iris Xe graphics | Multiple NVMe slots, support for up to 24TB SSD storage, two 10Gbps SFP+ ports and two 2.5Gbps RJ45 ports | Model server, NAS-like storage, networking, virtualization and homelab services | Iris Xe is not a strong standalone LLM accelerator; budget for a discrete GPU or eGPU |
MINISFORUM MS-S1 MAX: the capacity and expansion choice
Hardware that matters for local models
The MS-S1 MAX uses AMD’s Ryzen AI Max+ 395 with 16 CPU cores and 32 threads, integrated RDNA 3.5 graphics and an NPU. MINISFORUM specifies up to 128GB of LPDDR5x-8000 memory in a quad-channel design. Because the CPU and integrated GPU share that pool, a large allocation can be available to a model without installing separate DIMMs or a dedicated graphics card.
The 128GB ceiling is the important differentiator. A 36GB, 48GB or 64GB Mac Studio configuration may be faster on a model that fits comfortably, but a 128GB MS-S1 MAX configuration can accommodate larger quantized models or leave more room for a long context, operating-system overhead and other services. “Fits” does not mean “fast”: a model can load while generating slowly if bandwidth, kernels or memory traffic are unfavorable.
MINISFORUM claims 126 TOPS of peak AI performance. That is a vendor peak figure and should not be converted into a token-per-second expectation. Token generation uses the complete software and memory path, not just the NPU’s headline number.
Expansion and upgrade trade-offs
TechRadar reports a full-size PCIe x16 expansion slot and storage expansion. That gives the MS-S1 MAX a path to a discrete accelerator, high-speed network adapter or other PCIe device. It is a material advantage over the fixed internal design of a Mac Studio.
The trade-off is that the major CPU, GPU and RAM components are soldered. You must select the memory capacity at purchase; the PCIe slot does not make the system’s built-in memory upgradeable. Check the enclosure, power budget, physical clearance and operating-system support before assuming any desktop GPU will work as a drop-in addition.
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What the published benchmarks do and do not prove
TechRadar reported CrystalDiskMark sequential read of 6061.76 MB/s and write of 5374.64 MB/s, a Geekbench CPU multi-core score of 19,653, a Geekbench GPU score of 95,729 and a PCMark overall score of 8,425. These are useful indicators of storage and general-system behavior, but they are not llama.cpp generation results. Do not use them to claim that the MS-S1 MAX produces a particular number of tokens per second.
MINISFORUM also reports that a dual-unit cluster produced 10.87 tokens per second with a 235B Q4 model, and that a four-unit cluster ran DeepSeek-R1 671B Q4 (380GB, Q4_0 quantization). Those are vendor-reported multi-unit demonstrations. They show that distributed memory can make very large models possible, not how quickly one MS-S1 MAX runs them.
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Why it leads in the cited llama.cpp tests
Tom’s Hardware tested llama.cpp with native Metal and MLX support on Apple silicon. Its workloads included Qwen 3.6-35B-A3B, Gemma 4 12B and OpenAI gpt-oss-120b, all using four-bit quantizations. In those tests, the M4 Max beat AMD Strix Halo for prompt processing on the tested Qwen workload and led both Strix Halo and GB10 systems in decode throughput across the tested context depths.
The tested M4 Max has a 40-core GPU and a rated 546GB/s memory bandwidth. Tom’s Hardware’s overall conclusion was that the M4 Max GPU generated more tokens per second than competing systems with large unified-memory pools in its test set, while warning that memory bandwidth alone is not a reliable proxy for delivered performance. Model architecture changes the result, so treat this as evidence for the tested stack, not a permanent ranking of every compact AI computer.
Software experience
Metal and MLX are the practical advantage. Apple-specific kernels and mature builds can reduce the setup work for Ollama or llama.cpp users who want to download a model and begin generating. The exact backend still matters: a model may use MLX, Metal, CPU code or a combination, and new architectures can take time to receive optimized kernels.
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Mac Studio is an appliance-like choice. Memory is selected when ordering and is not user-upgradable. Apple provides four Thunderbolt 5 ports on the tested configuration, 10Gb Ethernet, HDMI 2.1 and an SD card reader, but external Thunderbolt devices are not equivalent to an internal PCIe slot for every GPU or accelerator workflow.
MINISFORUM MS-01: excellent server plumbing, weak native acceleration
Where the MS-01 excels
The MS-01 is built around service density rather than an integrated AI GPU. Versions use Intel Core i9-13900H or i9-12900H processors and Intel Iris Xe integrated graphics. It provides two 10Gbps SFP+ ports, two 2.5Gbps RJ45 ports, two USB4 ports and support for up to 24TB of SSD storage.
That combination makes it useful as a local model server, storage node, virtualization host, router-adjacent appliance or homelab machine. Large NVMe capacity lets you keep multiple quantizations and datasets online, while 10GbE can move models and serve inference to other computers without relying on Wi-Fi.
Why it is not the default local-LLM recommendation
Iris Xe graphics are not a strong standalone inference accelerator for modern large language models. The MS-01 can run CPU-oriented inference, but generation speed and model capacity will usually be constrained compared with a system designed around a large unified-memory GPU or a discrete accelerator. If local inference is the primary workload, plan for a compatible GPU or eGPU setup and account for its cost, power, cooling and software requirements. Without that addition, select the MS-01 for its networking and storage features, not for native token-generation performance.
How model size and quantization change the decision
Memory capacity comes before speed
A quantized model must fit in memory along with its runtime buffers, context and operating system. A nominal model-file size is not the whole requirement. Longer context windows and larger batches increase working memory, and leaving no headroom can cause swapping, failed allocations or severe slowdowns.
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The MS-S1 MAX’s 128GB option gives it more capacity than 36GB, 48GB or 64GB Mac Studio configurations. That makes it the safer compact choice for experimenting with larger quantized models on one box. Mac Studio remains preferable when the model fits and you value higher measured decode throughput.
Why a four-bit label is not a speed guarantee
“Q4” describes a quantization family, not a universal performance level. Different formats, kernels and model architectures have different overheads. The vendor’s 235B Q4 and 671B Q4 cluster claims therefore cannot be compared directly with Tom’s Hardware’s single-system tests of Qwen, Gemma and gpt-oss. Use the exact model, quantization and backend you intend to deploy when validating a purchase.
Software and operating-system choices
Choose the Mac when the tested Apple path matches your workflow
- Use macOS when you want Metal or MLX acceleration with minimal driver management.
- Prefer this route for a compact workstation dedicated to interactive generation, coding assistants or document chat where decode latency matters.
- Confirm that your target model has an MLX or Metal implementation; unsupported architectures may fall back to less efficient code.
Choose the MS-S1 MAX when flexibility is worth tuning
- Windows and Linux support gives you broader tool and container choices.
- Vulkan and ROCm paths can be useful, but support varies by model, runtime version and driver.
- Plan time to test kernel selection, GPU offload, context size and memory allocation rather than assuming a generic benchmark will transfer.
Use the MS-01 as a service host
Run the inference service on a machine with the accelerator and expose it over the MS-01’s network links, or add suitable acceleration to the MS-01 itself. This separates storage, networking and virtualization duties from generation hardware and can be more practical than forcing one tiny box to do everything.
Expansion, networking and storage comparison
| Need | Best match | Reason |
|---|---|---|
| Highest tested decode speed in the cited workloads | Mac Studio M4 Max | Tom’s Hardware measured it ahead of GB10 and Strix Halo systems in decode throughput. |
| More room for large quantized models in one compact chassis | MS-S1 MAX 128GB | Its maximum unified-memory configuration exceeds the cited 36GB, 48GB and 64GB Mac Studio options. |
| Internal accelerator or high-speed adapter | MS-S1 MAX | It exposes a full-size PCIe x16 slot, subject to physical and power constraints. |
| 10GbE plus many NVMe drives | MS-01 | Two 10Gbps SFP+ ports, two 2.5Gbps Ethernet ports and up to 24TB of SSD storage. |
| Simple, fixed desktop appliance | Mac Studio | Integrated Apple hardware and software, with external Thunderbolt expansion. |
Power, noise and price: what is actually established
MINISFORUM specifies 160W peak and 130W sustained power for the MS-S1 MAX. Those figures describe the platform’s stated power behavior, not a guaranteed wall-meter reading during every inference workload. A discrete GPU will add its own power, heat and acoustic requirements.
The supplied evidence does not establish a stable, cross-market price comparison for these systems. Regional listings, memory configuration, sales tax, storage and included accessories can change the total substantially. Compare like-for-like configurations at the time you buy, and include the cost of an accelerator, enclosure or network hardware if your plan requires one.
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A practical selection process
- List the exact models. Record parameter count, quantization format, expected context length and whether you need batching or only one interactive session.
- Check capacity first. Select a memory configuration that leaves headroom for runtime buffers and context. If the 128GB MS-S1 MAX is the only compact option that fits your target, speed comparisons are secondary.
- Choose the backend. Verify MLX/Metal support on macOS, or the Vulkan/ROCm/CPU path you plan to use on the MS-S1 MAX or MS-01.
- Decide whether expansion is part of the requirement. Pick the MS-S1 MAX if a future discrete GPU or PCIe adapter is central; pick Mac Studio if external Thunderbolt devices are sufficient.
- Separate server duties when appropriate. Use the MS-01 for storage, 10GbE and virtualization, with inference handled by a better accelerator elsewhere on the network.
- Test before standardizing. Measure prompt processing and decode tokens per second with your actual model, context and runtime. Do not substitute CrystalDiskMark, Geekbench or a vendor cluster result for that test.
Common failure modes and fixes
The model will not load
Reduce context or batch size, use a smaller quantization, close other memory-heavy applications and verify that the runtime is using the intended unified memory or GPU offload. If the model still exceeds capacity, move to the 128GB MS-S1 MAX configuration, a larger discrete-GPU workstation or a multi-node design.
Generation is far slower than expected
Confirm that acceleration is active rather than CPU-only, check the selected backend and driver, and compare the same model and quantization across systems. A high TOPS claim or memory-bandwidth number cannot diagnose a missing kernel or an unsupported architecture.
Long prompts collapse throughput
Prompt processing and decode are different phases. Measure them separately, then reduce unnecessary context, use a more suitable context setting or choose hardware with more memory headroom. Tom’s Hardware’s results varied by workload and context depth, so one short-prompt result should not be generalized.
The MS-01 becomes a noisy or expensive accelerator
Recalculate the complete system: GPU or eGPU, enclosure, power delivery, cooling and network path. If those additions dominate the budget, retain the MS-01 as a server and place the accelerator in a workstation designed for it.
Which one should you buy?
Buy Mac Studio M4 Max when speed and simplicity win
Select it for the fastest tested single-box decode in the cited llama.cpp workloads, macOS integration and a mature Metal/MLX path. It is the strongest default for interactive use when your chosen models fit the memory configuration you can afford.
Buy the MINISFORUM MS-S1 MAX AI workstation when capacity and expansion win
Select a 128GB configuration when larger quantized models, Windows/Linux tooling or future PCIe expansion matter more than the highest measured decode rate. Treat its 126 TOPS figure and general-system benchmarks as specifications, not as token-generation promises.
Buy the MS-01 workstation when infrastructure is the real job
Select it for 10GbE, multiple NVMe drives, virtualization and homelab services. Add a discrete accelerator only after confirming compatibility and total power and cooling requirements; its integrated Iris Xe graphics should not be the reason you choose it for local LLM inference.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsMove to a larger NVIDIA workstation when compactness stops being the priority
CUDA compatibility, high-throughput batching and image-generation workloads can justify a larger system. The evidence here does not include a directly tested compact NVIDIA model, so no numeric ranking is assigned to that category.
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