Short answer: Choose the Mac Studio M4 Max if your priority is the highest tested single-box token-generation speed and the smoothest Metal/MLX software path. Choose the MINISFORUM MS-S1 MAX with 128GB of memory if fitting larger quantized models, using Windows or Linux, or adding a discrete GPU matters more than peak decode speed. Choose the MINISFORUM MS-01 for 10GbE networking, multiple NVMe drives, virtualization and homelab services; its integrated Iris Xe graphics are not a strong primary inference accelerator without additional GPU hardware.
The decision in one view
Compact AI PCs make different compromises. The Mac Studio M4 Max spends its design budget on a fast, tightly integrated Apple Silicon system and a mature Metal/MLX software stack. The MINISFORUM MS-S1 MAX offers much more configurable unified memory, a Windows/Linux-friendly platform and a full-size PCIe slot. The MINISFORUM MS-01 is best understood as a connected storage and services node rather than a finished AI accelerator.
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| Priority | Best fit | Why |
|---|---|---|
| Fastest tested single-box decode | Mac Studio M4 Max | Tom’s Hardware’s 2026 llama.cpp tests put the tested M4 Max ahead of GB10 and AMD Strix Halo systems in decode throughput for the workloads measured. |
| Largest model capacity in a compact box | MINISFORUM MS-S1 MAX 128GB | Its Ryzen AI Max+ 395 platform supports up to 128GB of LPDDR5x-8000 unified memory, giving more headroom than 36GB, 48GB or 64GB configurations. |
| Windows/Linux flexibility and GPU expansion | MINISFORUM MS-S1 MAX | It combines a 16-core/32-thread processor, integrated RDNA 3.5 graphics and a full-size PCIe x16 expansion slot. |
| Networking, storage and homelab duties | MINISFORUM MS-01 | Two 10Gbps SFP+ ports, two 2.5Gbps Ethernet ports, multiple NVMe positions and support for up to 24TB of SSD storage suit server workloads. |
These are different recommendations, not a universal ranking. Model architecture, quantization, context length, backend support and whether you value prompt processing or decode speed can change the result.
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Memory determines whether the model fits
Local inference first requires enough fast memory for the model’s weights. A 4-bit 70-billion-parameter model has roughly 35GB of raw weight data before metadata, runtime allocations and the key-value (KV) cache used for context. Real files vary by quantization format, and long contexts can consume many additional gigabytes. The practical question is therefore not just “Can the weights fit?” but “Can the weights, context and operating system fit while leaving enough bandwidth for useful speed?”
#1 Best Overall
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A 128GB MS-S1 MAX configuration can accommodate substantially larger quantized models than compact Macs configured with 36GB, 48GB or 64GB. That does not mean every 70B-or-larger model will run quickly, or that every quantization will fit. A larger model may load successfully yet produce too few tokens per second for interactive use, especially with a long context or a backend that cannot use the integrated GPU efficiently.
Bandwidth determines how quickly data moves
During token generation, the processor repeatedly reads model data. High memory bandwidth can improve decode speed, but it is not a performance guarantee. Tom’s Hardware measured the M4 Max with a rated 546GB/s memory bandwidth and still cautioned that bandwidth specifications alone do not predict delivered inference performance. Kernel quality, cache behavior, quantization format and model architecture all matter.
Prompt processing and decode are different
Prompt processing (also called prompt ingestion or prefill) handles many input tokens at once. Decode generates the answer one token at a time. A computer can be excellent at prefill but less impressive during decode, or the reverse. If you mostly summarize long documents, examine prompt-processing results; if you chat interactively, decode throughput and first-token latency matter more.
MINISFORUM MS-S1 MAX: the capacity-and-expansion option
Hardware that matters for inference
- AMD Ryzen AI Max+ 395 with 16 CPU cores and 32 threads.
- Integrated RDNA 3.5 graphics and an NPU.
- Up to 128GB of LPDDR5x-8000 quad-channel unified memory.
- MINISFORUM specifies 160W peak and 130W sustained power.
- Major CPU, GPU and memory components are soldered, while a full-size PCIe x16 expansion slot and storage expansion are available.
- MINISFORUM lists a claimed peak AI performance of 126 TOPS. TOPS is a theoretical throughput figure, not a llama.cpp token-rate measurement.
The unified-memory design lets the CPU and integrated GPU draw from the same pool. That avoids copying a model between separate system RAM and VRAM, but the GPU still needs a supported backend and enough bandwidth to deliver good decode performance.
Rank #2
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Where the 128GB configuration helps
The 128GB model is the sensible starting point when your goal is a 70B-class model, multiple models kept ready to load, or a large context window. It also gives room for a model server, embeddings process and other services to run together. Smaller configurations can be better value for 7B, 13B or many 30B-class models, but memory capacity is difficult to add later because the LPDDR5x is soldered.
What expansion changes
The PCIe x16 slot is the MS-S1 MAX’s key distinction from a fixed compact desktop. It gives you a path to a discrete GPU when the integrated graphics are not fast enough or when a software stack is optimized for a particular accelerator. Check the chassis, power delivery, card length and cooling requirements before buying a GPU; the presence of a slot does not make every desktop card a drop-in installation.
Expansion also creates a trade-off. A discrete GPU can improve throughput and compatibility, but it adds cost, noise, power draw and setup complexity. If you want a quiet, self-contained appliance, the Mac Studio remains simpler.
Software choices
Windows and Linux provide broad tool choice, but the best path depends on the model and driver. Vulkan backends can be useful for broadly supported GPU inference. ROCm support may offer a stronger route for selected AMD hardware, but compatibility and tuning vary by operating-system version, driver and application. Treat “supports Vulkan” or “supports ROCm” as a starting point, not a promise of equal performance across every model.
Rank #3
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- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
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- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
Mac Studio M4 Max: the speed-and-software option
Relevant specifications
- The tested configuration uses an M4 Max with a 40-core GPU.
- Tom’s Hardware reports 546GB/s rated unified-memory bandwidth for that tested M4 Max.
- Connectivity includes four Thunderbolt 5 ports, 10Gb Ethernet, HDMI 2.1 and an SD card reader.
- Memory is unified and configured at purchase; it is not user-upgradable.
Apple’s unified architecture keeps the CPU and GPU close to the same memory pool, while Metal and MLX provide a mature path for Apple Silicon inference. The result is a compact system that usually needs less tinkering than a newly configured Windows/Linux accelerator stack.
What the independent test showed
Tom’s Hardware used llama.cpp with native Metal/MLX support and tested four-bit quantizations of Qwen 3.6-35B-A3B, Gemma 4 12B and OpenAI gpt-oss-120b. In that test setup, the M4 Max beat AMD Strix Halo on prompt processing for the tested Qwen workload and led both GB10 and Strix Halo in decode throughput across the context depths measured.
Those results establish an advantage for the cited workloads, not a permanent win for every model. A different architecture, quantization, context size or software release can narrow or reverse the gap. The same testing also found that a large unified-memory pool by itself does not guarantee higher token rates.
The Tool Desk
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The Mac Studio’s memory cannot be upgraded after purchase, and there is no internal PCIe slot for a discrete GPU. Thunderbolt can attach external storage and other peripherals, but it is not equivalent to an internal, high-bandwidth GPU connection for every inference workload. Select the memory tier you need at purchase and regard it as a permanent capacity decision.
Rank #4
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MINISFORUM MS-01: a server and storage specialist
What it includes
- Intel Core i9-13900H or i9-12900H variants.
- Integrated Intel Iris Xe graphics.
- Two 10Gbps SFP+ ports and two 2.5Gbps RJ45 Ethernet ports.
- Two USB4 ports.
- Support for up to 24TB of SSD storage.
This combination is excellent for a local model server, virtualization host, NAS-like storage role, containers, network services and fast transfers to other machines. The integrated Iris Xe graphics, however, are not a strong standalone accelerator for large local language models. If inference is the primary workload, budget for a compatible discrete GPU or an external GPU arrangement and verify the connection bandwidth and software support first.
Published system benchmarks are not inference benchmarks
TechRadar reported CrystalDiskMark results of 6061.76MB/s read and 5374.64MB/s write, Geekbench CPU multi-core of 19,653, Geekbench GPU of 95,729 and a PCMark overall score of 8,425 for its reviewed MS-S1 MAX system. Those figures describe storage and general system performance; they are not llama.cpp token-generation measurements and should not be used to rank the MS-01 against the Mac Studio for inference.
How the machines compare for real local-LLM jobs
| Workload or requirement | Mac Studio M4 Max | MINISFORUM MS-S1 MAX | MINISFORUM MS-01 |
|---|---|---|---|
| Interactive decode speed | Best result in the cited independent llama.cpp workloads. | Potentially capable, but no directly comparable independent token-rate result is established here. | Integrated graphics are not a strong primary choice. |
| 70B-class quantized model | Depends on the purchased memory tier, quantization and context; 36GB, 48GB and 64GB tiers have less headroom than 128GB. | 128GB provides the most capacity of these compact options. | System memory capacity and graphics acceleration are not positioned for this as a turnkey workload. |
| Models larger than 70B | Possible only when the selected memory configuration and quantization leave room for runtime overhead; capacity becomes the limiting factor quickly. | Best compact candidate for fitting larger quantized models, though speed may be modest without an added GPU. | Better suited to serving models from attached storage to another accelerator. |
| Windows or Linux tooling | Not applicable; macOS and Apple backends are the intended path. | Strongest fit, with Vulkan/ROCm options that vary by driver and model. | Strong fit for Linux servers, containers and virtualization. |
| Discrete-GPU upgrade | No internal PCIe GPU upgrade. | Full-size PCIe x16 expansion slot. | Plan around external or separately connected GPU hardware; integrated Iris Xe is not the target accelerator. |
| Networking and storage service | 10Gb Ethernet and Thunderbolt 5 are useful, but storage expansion is primarily external. | Storage expansion is available, with general-purpose desktop connectivity. | Two 10Gbps SFP+, two 2.5GbE ports and up to 24TB SSD support are the defining strengths. |
Can a 128GB mini PC run a 70B or larger model?
Often yes for a quantized model, but “runs” and “runs well” are different claims. A 128GB MS-S1 MAX has enough unified memory for many 70B four-bit builds and leaves more room for the KV cache than smaller compact systems. The exact result depends on the model file, quantization format, context length, runtime overhead and whether the selected backend can use the integrated GPU.
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- Read the model’s published file size and quantization. Do not infer the size from the parameter count alone.
- Reserve memory for the operating system, inference runtime, KV cache and any other models or services running at the same time.
- Choose a context length deliberately. Increasing context can consume substantial additional memory.
- Confirm that your runtime supports the model architecture and quantization on your operating system and GPU backend.
- Start with a short context and one request, then monitor memory use before enabling concurrent users or a larger context.
MINISFORUM reports that a dual-unit cluster ran a 235B Q4 large model locally at 10.87 tokens per second and that a four-unit cluster ran DeepSeek-R1 671B Q4 (380GB, Q4_0 quantization). These are vendor-reported multi-unit results, not evidence that one MS-S1 MAX can hold or deliver the same model by itself.
Best Value
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- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
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Which machine is faster in Ollama or llama.cpp?
For the specific llama.cpp setup tested by Tom’s Hardware, the M4 Max produced more decode tokens per second than the compared GB10 and AMD Strix Halo systems. That is the strongest directly cited speed evidence in this comparison. The test used native Metal/MLX support and the three stated four-bit model families, so treat it as a workload-specific result rather than a universal Ollama leaderboard.
Ollama performance can differ from a published llama.cpp run because packaging, backend selection, model conversion, context settings and application versions change. On the Mac, verify that the model is using the Metal path rather than falling back to CPU. On the MS-S1 MAX, verify whether the runtime is using Vulkan, ROCm or CPU, and compare the same model file, quantization, context and concurrency on both machines.
A fair home comparison procedure
- Use the same model family, quantization and context length on both systems.
- Update the inference application and graphics drivers, then record their versions.
- Warm up each model with the same prompt before recording decode speed.
- Measure prompt processing and decode separately; do not combine them into one number.
- Run at least three trials and report the median, while noting whether other services were active.
- Record memory use, temperature, power mode and whether any layer offloading occurred.
Do you need a discrete GPU or an eGPU?
You do not need one for every local-LLM workload. The Mac Studio can deliver strong results with its integrated GPU, and the MS-S1 MAX can run models from its unified memory. A discrete GPU becomes more compelling when you need CUDA-specific software, higher batch throughput, image-generation workloads, several simultaneous users or faster performance on models that do not run efficiently on the integrated backend.
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When the MS-S1 MAX’s PCIe slot is worthwhile
- You already own a compatible GPU and want to reuse it.
- Your preferred inference framework is substantially better supported on that GPU than on the integrated RDNA graphics.
- You need more dedicated VRAM bandwidth for batching or multimodal workloads.
- You accept higher power, heat, noise and configuration complexity.
When an external GPU is a poor fit
- The enclosure and GPU cost approaches that of a larger workstation.
- The connection becomes the bottleneck for the model or batch size.
- Your operating system or inference framework has incomplete external-GPU support.
- You want a quiet, appliance-like setup with minimal maintenance.
For very high-throughput batching, broad CUDA compatibility or demanding image generation, a larger NVIDIA workstation may be a better architectural choice than either compact system. This comparison does not include a directly tested compact NVIDIA model, so it does not assign a numeric ranking.
Power, noise and price considerations
Power and thermals
MINISFORUM specifies 160W peak and 130W sustained power for the MS-S1 MAX platform. Adding a discrete GPU increases the system’s heat and acoustic envelope beyond those figures. The Mac Studio’s integrated design avoids a separate GPU and its associated power supply, while the MS-01’s server-oriented storage and networking configuration may spend more of its budget on drives and network hardware than on inference acceleration.
Price is region- and configuration-dependent
A stable cross-market price comparison is not established here. Memory tier, barebones versus complete configuration, storage, taxes, shipping and regional availability can change the total substantially. Compare complete systems with the same RAM capacity, SSD size and warranty terms at the time you buy rather than comparing base-model headlines.
Choose by your actual workload
Choose Mac Studio M4 Max when
- You want the fastest tested single-box decode in the cited llama.cpp workloads.
- You prefer macOS with a mature Metal/MLX path and minimal driver tuning.
- Your models fit the memory tier you can purchase, including context and runtime overhead.
- You value a quiet, fixed appliance more than internal GPU expansion.
Choose MINISFORUM MS-S1 MAX 128GB when
- You need the largest compact unified-memory pool for larger quantized models.
- You want Windows or Linux tools and the option to tune Vulkan or ROCm backends.
- You may add a discrete GPU through the PCIe x16 slot later.
- You accept that capacity does not automatically equal Mac Studio-level decode speed.
Choose MINISFORUM MS-01 when
- Your priority is 10GbE, NVMe capacity, virtualization, containers or homelab services.
- You want a model server that can hand work to another accelerator on the network.
- Local inference is secondary, or you have a clear plan for compatible discrete GPU hardware.
Choose a larger workstation when
- You need CUDA-first compatibility, high-throughput batching or serious image-generation performance.
- You can trade compactness for larger cooling, power and GPU options.
- You need a directly validated production configuration rather than a small desktop with expansion compromises.
Bottom line for local inference
The Mac Studio M4 Max is the safest recommendation for fast, straightforward single-machine generation: independent testing favors it in the cited llama.cpp workloads, and Metal/MLX reduces software friction. The MINISFORUM MS-S1 MAX is the better strategic buy when model capacity and expansion matter more than the highest tested decode rate, especially in its 128GB configuration. The MINISFORUM MS-01 is a capable networked storage and homelab platform, but it should not be selected as a primary local-LLM accelerator without additional GPU hardware.
Quick Recap
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