Mac vs PC for Local LLMs (2026): Speed vs Memory

For local LLMs, a PC with an Nvidia graphics card is the better choice if your models fit in its VRAM: it is faster at both reading prompts and generating text, and nearly every AI tool supports CUDA first. A Mac is the better choice if you want to run very large models, 70B and beyond, in a single quiet machine, because Apple’s unified memory goes up to 128GB on the M5 Max and 512GB on the M5 Ultra. The deciding question is simple: do you need speed, or do you need capacity?

This comparison looks at the platforms as they stand in October 2026, after Apple’s August launch of the M5 Max and M5 Ultra Mac Studio and with Nvidia’s RTX 50 series, DGX Spark and AMD’s Strix Halo machines on the PC side. Performance numbers come from named independent sources and Apple’s published specifications.

Verdict at a glance

The core difference: memory architecture

On a PC, a graphics card has its own dedicated memory (VRAM). It is extremely fast, but limited in size: 16GB on most mid-range cards and 32GB on the RTX 5090. If a model does not fit, part of it spills into slower system RAM and speed collapses.

On a Mac, the CPU and GPU share one pool of unified memory. You can configure it much larger than any consumer graphics card, and the GPU can use most of it for model weights. The trade-off is bandwidth: the M5 Max delivers 614 GB/s, roughly a third of the RTX 5090’s 1,792 GB/s. Since token generation speed is bound by memory bandwidth, a model that fits on both machines will generate faster on the Nvidia card.

Apple’s figures come from its August 2026 Mac Studio announcement; the Strix Halo and DGX Spark figures come from Context Studios’ 2026 comparison.

Speed: where the PC wins

Small and medium models

For anything that fits in 32GB, a high-end Nvidia GPU is several times faster than any unified-memory machine. The llama.cpp community scoreboard records the RTX 5090 at roughly 290 to 300 tokens per second generating on a Llama 2 7B Q4_0 model, against about 120 tokens per second for the M5 Max. In the LMSYS data set, the RTX 5090 produced 205.5 tokens per second on GPT-OSS 20B.

Prompt processing

Before a model replies, it has to read your input. This step depends on compute power, and it is where Nvidia’s lead is largest. LMSYS measured the RTX 5090 at 8,519 tokens per second of prefill on GPT-OSS 20B. Apple says the M5 generation adds Neural Accelerators to every GPU core and claims large prompt-processing gains over earlier chips, but discrete Nvidia GPUs still lead. If you paste long documents, analyse codebases or run coding agents, this difference is something you will feel.

Reasons to pick a PC for speed: Fastest generation and prompt processing for models that fit; mature CUDA tooling; you can add or swap GPUs later.

Reasons to skip: VRAM caps model size; high power draw and heat; graphics card prices are inflated by the 2026 memory shortage.

The catch: The moment a model exceeds your VRAM, the PC’s speed advantage disappears.

Capacity: where the Mac wins

70B dense models

A dense 70B model at 4-bit needs around 40GB or more, which does not fit on any single consumer graphics card. A 128GB M5 Max runs it entirely in memory. Community figures compiled by llmcheck.net put the M5 Max at about 15 to 18 tokens per second on dense 70B models, and other guides give a 12 to 18 range for the 128GB configuration. That is comfortable reading speed. For comparison, LMSYS measured just 2.7 tokens per second for a dense 70B model on the DGX Spark, whose bandwidth is less than half the M5 Max’s.

Very large and mixture-of-experts models

The M5 Ultra is in a class of its own for capacity. Apple offers it with up to 512GB of unified memory at 1.2 TB/s, a 50% bandwidth increase over the M3 Ultra. That is enough for 200B to 400B-class models. Apple says the 512GB configuration ships in late October 2026; some reports suggest orders may slip further. ModelFit’s estimates for a 256GB M5 Ultra include roughly 14 tokens per second for Qwen3 235B-A22B and about 43 for GPT-OSS 120B, but those are estimates rather than measurements.

Reasons to pick a Mac for capacity: Huge memory in one quiet box; much more bandwidth than other unified-memory machines; low power use; laptops get the same bandwidth as the desktop M5 Max.

Reasons to skip: Slower than Nvidia for models that fit in VRAM; memory cannot be upgraded after purchase; high-memory configurations are expensive.

The catch: You pay Apple’s prices for memory upfront, and you cannot add more later.

The middle ground: unified-memory PCs

PCs now offer their own unified-memory option. AMD’s Ryzen AI Max+ 395 (Strix Halo) mini PCs and Nvidia’s DGX Spark both ship with 128GB. On GPT-OSS 120B, HardwareCorner’s llama.cpp comparison measured 38.6 tokens per second on the Spark and 34.1 on the AMD machine, with the Spark far ahead on prompt processing (1,723 against 340 tokens per second). AMD’s own figures claim the Strix Halo platform is 4% to 14% faster in token throughput on several models, so results depend on the software stack.

Compared with a Mac, these machines usually cost less for 128GB, but their bandwidth is less than half of an M5 Max. They suit mixture-of-experts models, where only a fraction of the weights is active per token, better than dense 70B models.

The catch: Strix Halo can typically assign only about 96GB of its 128GB to the GPU, and DGX Spark runs Linux only.

Software and ecosystem

If you are a developer building on the same stack you will deploy to in the cloud, Nvidia’s CUDA ecosystem is the safer bet. If you mainly want to chat with large models, summarise documents and run local assistants, the Mac software stack is mature and simple to use.

Cost, power and noise

Exact prices move constantly during the memory shortage, so we compare in tiers. A PC with an RTX 5090 sits in the premium tier, and the card alone has been selling well above its launch price through 2026. A 128GB M5 Max Mac is also premium, while a 256GB or 512GB M5 Ultra is flagship territory. Strix Halo mini PCs are the most affordable way to get 128GB.

Power and noise strongly favour the Mac and the unified-memory mini PCs. A high-end gaming GPU can draw several hundred watts under sustained AI load, which means more heat and fan noise in a home office. A Mac Studio runs large models quietly on a fraction of that power.

How to decide

1. List the models you want to run. Note their size at 4-bit. If everything is 32B or smaller, a PC GPU is the fastest choice.
2. Decide how much speed matters. For chat, 15 tokens per second is fine. For coding agents and long documents, prompt processing speed matters a lot, which favours Nvidia.
3. Consider what else the machine does. If it is also your gaming PC, the PC wins. If it is also your everyday Mac or laptop, the Mac wins.
4. Think about upgrades. A PC lets you swap the GPU in a couple of years. A Mac’s memory is fixed at purchase, so buy more than you think you need.
5. Check your software. If your workflow depends on CUDA-only tools, buy Nvidia.

Real-world scenarios: which platform fits you

The gamer who wants to try local AI

You already own a gaming PC with a 12GB or 16GB graphics card. Do not buy anything yet. Install LM Studio or Ollama, download an 8B to 14B model at 4-bit, and see how you use it. A modern 16GB card runs these models far faster than you can read. If you later want bigger models, a GPU upgrade keeps your gaming benefits too, which a Mac cannot offer.

Pick: PC. The catch: You will hit the VRAM ceiling quickly if you get serious about larger models.

The writer or researcher who wants a private assistant

You want to summarise documents, draft text and ask questions about your own files without sending anything to the cloud. Quality matters more than raw speed, so larger models are attractive. A Mac with 64GB to 128GB of unified memory runs 70B-class models quietly on your desk and doubles as your everyday computer.

Pick: Mac with M5 Max and plenty of memory. The catch: Long documents take longer to process than on an Nvidia GPU, because prompt processing is slower.

The developer building AI apps

You write code that calls models through an API, test agents and may fine-tune smaller models. Most production AI runs on Nvidia hardware in the cloud, and tools like vLLM, PyTorch and many fine-tuning libraries are built around CUDA. A PC with an RTX 5090, or a DGX Spark if you want 128GB in a compact Linux box with the same software stack as Nvidia’s data-centre systems, keeps your local setup close to production.

Pick: PC with Nvidia GPU or DGX Spark. The catch: The Spark’s 273 GB/s bandwidth makes dense 70B models slow; LMSYS measured 2.7 tokens per second.

The enthusiast who wants the biggest open models

You want to run the largest open-weight models available, in the 200B to 400B range, at home. No consumer PC graphics card has enough memory for this, and multi-GPU workstation builds get expensive and loud quickly. The Mac Studio with M5 Ultra and 256GB to 512GB of unified memory is the simplest way to do it in one box.

Pick: Mac Studio M5 Ultra. The catch: It sits in the flagship price tier, and Apple’s 512GB configuration ships later than the rest of the range.

The budget-conscious tinkerer

You want to experiment with 100B-class mixture-of-experts models without paying flagship prices. A Ryzen AI Max+ 395 mini PC with 128GB is the cheapest way to fit those models in memory, and it runs Windows or Linux. HardwareCorner measured it at 34.1 tokens per second on GPT-OSS 120B, which is very usable for chat.

Pick: PC (Strix Halo). The catch: Prompt processing is slow compared with Nvidia hardware, and software support for AMD’s ROCm stack is less mature than CUDA.

Common mistakes

• Buying a Mac with too little memory. A 36GB Mac Studio is great for 14B to 30B models, but cannot run 70B. You cannot upgrade later.
• Buying a fast GPU for models that will not fit. A 16GB card is not a 70B machine, no matter how fast it is.
• Comparing only tokens per second. Prompt processing can dominate total wait time for long inputs.
• Trusting estimates as benchmarks. Several popular sites publish estimated speeds based on bandwidth. ModelFit, for example, labels its numbers as estimates. Look for measured results.

How we compared

We compared Mac and PC options using manufacturer specifications, independent lab measurements from the sources named above, and owner reports from the llama.cpp and MLX communities. We did not bench-test these units ourselves. Some figures, especially for the new M5 Ultra, are community-reported or estimated, and we have labelled them as such. Software updates can change results significantly.

Sources

• Apple Newsroom: Apple introduces new Mac Studio with M5 Max and M5 Ultra (August 2026)
• Apple: MacBook Pro technical specifications (M5 Max)
• LMSYS performance data, as compiled by Spheron
• llama.cpp community scoreboard and benchmark threads
• HardwareCorner: DGX Spark vs Ryzen AI Max+ 395 llama.cpp comparison
• Context Studios: Local AI Hardware Guide 2026
• llmcheck.net: Apple Silicon LLM benchmarks
• ModelFit: Mac Studio M5 Ultra local LLM estimates
• AMD: Ryzen AI Halo throughput claims

Frequently Asked Questions

Is a Mac good for running local LLMs?

Yes, especially for large models. Apple’s unified memory, up to 128GB on the M5 Max and 512GB on the M5 Ultra, lets a Mac run models that do not fit on any single consumer graphics card.

Is an RTX 5090 faster than a Mac for LLMs?

For models that fit in its 32GB of VRAM, yes, by a wide margin. The llama.cpp scoreboard shows roughly 290 to 300 tokens per second on a 7B model for the 5090 against about 120 for the M5 Max.

Can a Mac run a 70B model?

Yes, with 64GB or more of unified memory, and 128GB is more comfortable. Community figures put a 128GB M5 Max at about 12 to 18 tokens per second on dense 70B models.

Is MLX better than llama.cpp on a Mac?

MLX is generally the fastest option on Apple Silicon, and Ollama uses an MLX backend on Macs. llama.cpp with Metal is also well supported and offers more model formats.

What about AMD Strix Halo versus a Mac?

A Ryzen AI Max+ 395 mini PC gives you 128GB for less money, but at about 256 GB/s, less than half the bandwidth of an M5 Max. It works best with mixture-of-experts models.

Which is better for fine-tuning?

A PC with an Nvidia GPU. Most fine-tuning tools are built around CUDA and PyTorch, and Nvidia’s compute advantage is larger for training than for inference.

Can I use a MacBook Pro for local AI?

Yes. The 16-inch MacBook Pro with M5 Max offers the same 614 GB/s bandwidth and up to 128GB as the desktop chip, which makes it the most capable laptop for local LLMs.

Should I wait for new hardware?

The M5 Ultra just launched, and Nvidia’s next consumer generation has slipped well into the future according to leakers, so there is little reason to wait if you need a machine now.