You can run a local LLM on a Mac mini, a Strix Halo mini PC or an NVIDIA graphics card. In 2026 the price gap between them flipped. A renewed RTX 3090 now costs about $1,900 on Amazon and an in-stock RTX 5090 $8,000 or more, while Apple sells a 64GB Mac mini for $2,699. That Mac mini has no dedicated GPU and no VRAM, yet it holds a bigger model than either card. You give up speed, and how much depends on the model you run.
This Mac mini vs mini PC vs GPU comparison answers the graphics card question first. Then it lines up the M6 and M5 Pro Mac mini, the M5 Max Mac Studio, Ryzen AI Max+ 395 boxes and NVIDIA cards on memory, bandwidth, speed and price. The GPU numbers are our own, and the Apple and AMD figures come from linked benchmarks. Prices and stock below were checked in October 2026.
Does the Mac mini have a dedicated GPU?
No. The Mac mini’s GPU is part of the Apple chip: 12 cores on the M6, and 16 or 20 cores on the M5 Pro, according to Apple’s spec page. It shares one pool of unified memory with the CPU, so there’s no separate graphics card and no separate VRAM.
There is also no PCIe slot, and Apple’s own support page says an eGPU needs a Mac with an Intel processor. The one exception is Tiny Corp’s TinyGPU driver, announced in April 2026, which runs NVIDIA Ampere and newer or AMD RDNA3 and newer cards over Thunderbolt or USB4 with SIP left on, but only for compute through tinygrad: no display output, and no MLX, llama.cpp or Ollama. For the everyday local LLM tools, whatever memory you order is still the GPU memory you’ll have for the life of the machine, so you size it at checkout.
That shared pool is the whole argument for a Mac in local AI. A graphics card can only load a model that fits its VRAM, while the Mac’s GPU can address most of system memory. macOS does not hand you all of it, though. By default the GPU can wire about three quarters of RAM, a community-measured figure because Apple doesn’t publish the formula, which works out roughly like this:
- Mac mini M6, 16GB: about 11 to 12GB for a model at 153 GB/s. That’s room for an 8B model and a modest context.
- Mac mini M6, 24GB: about 16 to 18GB at 170 GB/s, enough for a 14B or a 20B mixture-of-experts model.
- Mac mini M5 Pro, 64GB: about 48GB at 307 GB/s. A 32B model fits with plenty of context, and a 4-bit 70B squeezes in with little left over.
You can raise that cap with sudo sysctl iogpu.wired_limit_mb=57344 on a 64GB Mac mini, which gives the GPU 56GB and leaves 8GB for macOS. It takes effect immediately and resets at the next reboot, as the Metal VRAM cap notes document. Leave at least 8GB for macOS. The unified memory vs VRAM explainer covers why the two designs behave so differently.
So what is the Mac mini’s graphics card equivalent? On capacity, a 64GB Mac mini holds more model than any consumer NVIDIA card, since the RTX 5090 stops at 32GB.
On speed it sits well below an RTX 3090. Token generation is bound by memory bandwidth, and the M5 Pro reads at 307 GB/s against the 3090’s 936 GB/s. By our estimate from the published llama.cpp numbers, an M5 Pro generates about 52 tokens a second on Llama 3.1 8B at 4-bit, where our RTX 3090 measured 147.1. If your model fits the card, the card is roughly 2.8 times faster, and that ratio drives every recommendation below.
Quick picks by what you run
For most people the decision comes down to the largest model they’ll run every day, then how quiet the machine needs to be. Here is where we’d put your money:
- Cheapest way into local 8B models: Mac mini M6, 16GB. About $780 on Amazon, under Apple’s $899, silent, and fast enough for chat on an 8B.
- Best Mac mini for local LLMs: the M5 Pro. Apple’s price for the 64GB build is $2,699, and that’s the one to order if 32B models are the plan. The 24GB model is about $1,600 on Amazon.
- Fastest for the money up to 32B: a renewed RTX 3090. We measured 38.9 tokens a second on Qwen3 32B, about three times our estimate for the M5 Pro. Only the far pricier 5090 beat it.
- Most model per dollar: a 128GB Strix Halo box like the Bosgame M5. About $3,600, and it runs gpt-oss-120b at around 50 tokens a second.
- Dense 70B at 4-bit, fastest: two RTX 3090s. Our dual-card build ran Llama 3.3 70B at 4-bit at 31.9 tokens a second.
- Dense 70B, quietest: a 128GB Mac Studio with the M5 Max. Around 11 tokens a second by our estimate, in a box that doesn’t need a 1000W power supply.
Notice what is missing: the RTX 5090. It is the fastest single card we’ve tested, but at $8,000 or more it costs about as much as three 64GB Mac minis. It still cannot hold a 70B at 4-bit.
How we got the numbers
Every NVIDIA figure on this page is ours. We rented an RTX 3090 and an RTX 5090 on vast.ai in October 2026, built the same llama.cpp release on both and ran identical llama-bench tests, which the RTX 3090 vs RTX 5090 benchmarks publish in full. The two-card 70B result comes from our dual RTX 3090 build, run on the same llama.cpp release with both cards capped at 290 W.
We have not had an M6, M5 Pro or Strix Halo machine on our bench, so those numbers are cited, and each one names its source.
Apple speeds start from the llama.cpp Apple Silicon table, which runs the same Llama 2 7B file on every chip. Where you need a bigger model, we scaled that result by file size, and those figures carry an “estimate” label. That method gives an M5 Max about 11 tokens a second on a 4-bit 70B, the same estimate our mini PC for local AI guide reached on its own. Prices come from Amazon’s product API and Apple’s configurator on 21 October 2026.
Mac mini vs mini PC vs GPU at a glance
Read the table across the two speed columns. The 8B column shows how fast each machine runs a model that fits everywhere. The 70B column shows which machines you can load a dense 70B on at all.
| Machine | Memory for the model | Bandwidth | 8B Q4 generation | Dense 70B generation | Price, Oct 2026 |
|---|---|---|---|---|---|
| Mac mini M6, 16GB | about 11 to 12GB | 153 GB/s | about 25 tok/s (estimate) | does not fit | about $780 (Amazon) |
| Mac mini M5 Pro, 64GB | about 48GB | 307 GB/s | about 52 tok/s (estimate) | about 6 tok/s (estimate) | $2,699 (Apple price) |
| Mac Studio M5 Max, 128GB | about 96GB | 614 GB/s | about 93 tok/s (estimate) | about 11 tok/s (estimate) | $5,099 (Apple price) |
| Strix Halo 395, 128GB | up to 96GB (BIOS default; more on Linux via GTT) | 256 GB/s | no comparable run | 4.1 tok/s (72B Q5, cited) | about $3,600 (Bosgame M5) |
| RTX 3090, 24GB | 24GB | 936 GB/s | 147.1 tok/s (measured) | does not fit | about $1,830 to $1,880 renewed |
| RTX 5090, 32GB | 32GB | 1,792 GB/s | 277.8 tok/s (measured) | does not fit at 4-bit | about $8,000 to $8,500 |
| Two RTX 3090s | 48GB | 936 GB/s each | not run | 31.9 tok/s (measured) | about $3,700 for the cards |
Two things stand out. From 64GB up, every Apple and AMD machine holds more model than any single NVIDIA card. Every NVIDIA card is faster than all of them on the models it can hold. The Mac mini is the cheapest way into the capacity column, and the Mac Studio is the only unified-memory box here that comes close to chat speed on a dense 70B.
1. Mac mini (M6 or M5 Pro)
The Mac mini wins when you want a quiet, low-power box for 8B to 32B models and do not want to build anything. Apple rates it at 5 dBA at idle and 155W maximum continuous power, so it can sit on a desk or a shelf and run all day. The chip you pick decides what your Mac mini is good for, because the M5 Pro has nearly twice the M6’s memory bandwidth and is the only Mac mini that can be ordered with 48GB or 64GB.
The base M6 with 16GB is about $780 on Amazon. Its 153 GB/s bus gives you around 25 tokens a second on an 8B model by our estimate, which is comfortable for chat.
Prompt reading is quick for a small chip too: the table’s M6 entry, an 8-core GPU part at the same 170 GB/s as the 24GB Mac mini, processed 989 tokens a second on the 7B test. The M5 Pro with 24GB and a 512GB SSD is about $1,600 on Amazon, sold by Amazon itself. In the llama.cpp table the 16-core M5 Pro generated 67.6 tokens a second on the 7B file against 49.6 for the 16-core M4 Pro it replaces, so the new chip is a real step up.
Memory is where Apple’s pricing bites. On the M5 Pro, Apple charges $600 to go from 24GB to 48GB and $1,000 for 64GB. No 64GB Mac mini was listed on Amazon when we checked. You order that build from the Apple store, where Apple’s price is $2,699. At 64GB you can run a Qwen3 32B at 4-bit (19.8GB) at roughly 13 tokens a second by our estimate, and a Llama 3.3 70B at 4-bit (42.5GB) loads but leaves only about 5GB for context unless you raise the wired limit.
Expect about 6 tokens a second on that 70B, which is batch speed, not chat.
Pass on the Mac mini if your models already fit in 24GB and speed matters more than noise, because a renewed 3090 is about three times faster there. It is also the wrong box for CUDA-only tools such as TensorRT-LLM and most fine-tuning scripts, and the memory and SSD are soldered, so buy the capacity you’ll need in two years. For a Mac mini that runs agents rather than big models, our Mac mini for AI agents guide covers the M6 configs and the storage and UPS gear around them.
2. Mac Studio (M5 Max)
The Mac Studio wins on a dense 70B in a quiet box, and it is the Apple machine to buy when the Mac mini’s 307 GB/s is the bottleneck. The M5 Max with the 40-core GPU reads memory at 614 GB/s, twice the M5 Pro, and you can order it with 128GB.
In the llama.cpp table that chip generated 119.9 tokens a second on the 7B file, and scaling by file size puts a 4-bit Llama 3.3 70B at about 11 tokens a second. Nobody has published a dense 70B run on the M5 Max yet, so treat that as an estimate.
Mixture-of-experts models are where it pulls away. Hardware Corner measured gpt-oss-120b at 87.9 tokens a second with a 4K context, on a MacBook Pro with the same M5 Max and 128GB. Strix Halo boxes manage 49 to 54.5 on the same model, though the stacks differ: Hardware Corner ran MLX on a build it labels MXFP4 in 8-bit, while the Strix Halo figures are llama.cpp on ROCm and Vulkan with the MXFP4 GGUF.
The base Mac Studio with 36GB is about $2,300 on Amazon, but it has the 32-core GPU at 460 GB/s and too little memory for a 70B. The 128GB build is configured at Apple, where Apple’s price is $5,099 with a 512GB SSD.
Look elsewhere if you need a dense 70B fast rather than quiet: two RTX 3090s run it nearly three times faster, though the two cards alone cost about $3,700 before you build the tower around them. If your models stop at 32B, the Mac mini M5 Pro does that job for about half the price. Above 128GB you need the M5 Ultra at 1.2 TB/s. It starts at $5,499 with 96GB, but 256GB and 512GB require the 36-core CPU and 80-core GPU version, which starts at $6,799, and Apple lists the 512GB option as coming late October.
3. Strix Halo mini PC (Ryzen AI Max+ 395)
A Strix Halo mini PC wins on model size per dollar. The AMD Ryzen AI Max+ 395 puts up to 128GB of LPDDR5X in one shared pool and lets the BIOS assign up to 96GB of it to the integrated Radeon 8060S. On Linux you can raise the GTT limit further; the seehiong EVO-X2 run cited below set about 115 GiB. The Bosgame M5 with 128GB and a 2TB SSD was about $3,600 on Amazon when we checked, which is $1,500 under a 128GB Mac Studio.
The bandwidth is the catch. At 256 GB/s it is slower than the Mac mini M5 Pro, so dense models crawl: a published llama.cpp run on an EVO-X2 managed 4.1 tokens a second on Qwen2.5 72B at Q5_K_M. Mixture-of-experts models are the reason to own one, since gpt-oss-120b runs at 49.0 tokens a second on ROCm in that run and 54.5 on Vulkan in a separate llama-bench run on the same chip.
Prompt processing is the other weak spot: about 459 tokens a second on gpt-oss-120b, so a long document you paste in takes a while to read before the first word comes back.
All the 395 boxes share one chip, so they decode at the same speed and you choose on ports, noise and price. The GMKtec EVO-X2 with 128GB and 2TB is about $3,650 on Amazon, and our Ryzen AI Max+ 395 mini PC comparison sorts the rest box by box. Wrong pick if your models fit in 24GB, if you feed long contexts all day, or if your tools assume CUDA.
Strix Halo vs Mac mini at 64GB
At 64GB the two land surprisingly close, and the choice is mostly about the operating system:
| GMKtec EVO-X2, 64GB | Mac mini M5 Pro, 64GB | |
|---|---|---|
| Price, Oct 2026 | about $2,200 (Amazon) | $2,699 (Apple price) |
| Memory bandwidth | 256 GB/s | 307 GB/s |
| Memory the GPU can use | roughly 48GB by default; more on Linux via GTT | about 48GB (default cap) |
| Software | Linux or Windows, ROCm or Vulkan | macOS, Metal and MLX |
| Fast ports | 2x USB4, 2.5GbE | 3x Thunderbolt 5, 2.5GbE (10GbE option) |
The EVO-X2 64GB is about $500 cheaper, runs Linux, and uses the same chip as the 128GB boxes. The Mac mini gives you about 20% more bandwidth, so it generates a little faster on the same model, and it is quieter and easier to live with. We’d pick the EVO-X2 for a headless Linux inference server and the Mac mini for a desk machine that also runs an editor and a browser all day.
4. NVIDIA graphics card
A graphics card wins on speed for anything that fits in its VRAM, and it is the only path here with full CUDA support. On our rented hardware a 24GB RTX 3090 generated 147.1 tokens a second on Llama 3.1 8B and 38.9 on Qwen3 32B, and it read a 32B prompt at 1,304 tokens a second. That’s roughly three times what you’d get from a Mac mini M5 Pro on the same models, by our estimate.
Prices are what changed this year. The renewed RTX 3090 Founders Edition was about $1,880 on Amazon and a renewed MSI RTX 3090 Ventus 3X about $1,830, with only one or two of each left.
The RTX 5090 is faster again, at 277.8 tokens a second on the 8B and 74.5 on the 32B, and it ran a 3-bit Llama 3.3 70B entirely on the card at 52.8. In stock, though, that meant listings like the ZOTAC RTX 5090 AMP Extreme Infinity at about $8,500, which is hard to justify against any Mac in this comparison.
For a dense 70B at 4-bit, two 3090s are the fastest option we’ve measured: 31.9 tokens a second with llama.cpp’s tensor split, and 48GB of combined VRAM. The two-card build guide lists the parts and the PCIe layout that makes it work.
Pass on the GPU path if you do not want a tower, a big power supply and real fan noise. One 5090 needs a 1000W unit and hit its 575 W limit in our 70B runs, and our dual-3090 build uses a 1500W Corsair HX1500i. The wider field, from 16GB cards to 96GB workstation parts, is in the best GPU for LLMs guide.
What decides the purchase
Start from the model, not the machine. Size the largest model you’ll run every day with the LLM VRAM calculator, then check these in order:
- Capacity first: the model plus its context has to fit in the memory the GPU can use. A 4-bit 70B is 42.5GB, so it is out on any single consumer card and tight on a 64GB Mac.
- Bandwidth second: once it fits, generation speed follows memory bandwidth almost one to one. That’s why 936 GB/s beats 307 GB/s by about three times.
- Dense or mixture-of-experts: gpt-oss-120b reads only a few billion parameters per token, so it runs far faster than a dense 70B on the same unified-memory box.
- Prompt length: long documents and coding agents lean on prompt processing, where NVIDIA leads, the M5 Max follows and Strix Halo trails.
- Software stack: CUDA runs everything; MLX and llama.cpp cover the Mac well; ROCm and Vulkan cover Strix Halo with more setup.
- Upgrades and noise: Apple and Strix Halo memory is soldered, while a GPU tower can take another card later at the cost of power and noise.
The last two points decide more purchases than the benchmarks do. If your box will sit in a living room or a home office, the Mac or the mini PC wins even when the card is faster on paper.
Match the machine to the model you run
If you only remember one thing, buy for the largest model you’ll run weekly and the speed you’ll tolerate on it:
- 8B to 14B models, quiet and cheap: Mac mini M6. The 16GB build handles an 8B; take 24GB for 14B.
- Up to 32B, fast without paying 5090 prices: a renewed RTX 3090 in a PC you already own.
- Up to 32B, silent and simple: Mac mini M5 Pro with 48GB or 64GB from Apple, or the EVO-X2 64GB if you want Linux.
- gpt-oss-120b and other big MoE models: a 128GB Strix Halo box for the price, or a 128GB Mac Studio M5 Max for the speed.
- Dense 70B every day: two RTX 3090s for speed, the 128GB Mac Studio for quiet. A 64GB Mac mini holds it, but at batch speed.
The Mac mini’s missing GPU matters less than it sounds, then. It trades speed for capacity, and at 2026 graphics card prices that trade favors the Mac if you run models between 14B and 32B and do not want to build a tower.
