Mac vs PC for local LLM work comes down to one trade-off: capacity versus speed. An Apple Silicon Mac shares one large pool of unified memory between CPU and GPU, so a 64GB or 128GB Mac can load 70B-class models that no single consumer graphics card can hold. A PC with an NVIDIA RTX 50-series GPU has far less memory for models (16GB to 32GB) but much higher bandwidth and the full CUDA software stack, so anything that fits in its VRAM runs faster.
The short version for 2026: pick a PC with an RTX 5070 Ti, 5080 or 5090 if you mostly run 7B to 32B models, want maximum tokens per second or plan to fine-tune. Pick a Mac Studio or Mac mini with lots of memory if you want 70B+ models on one quiet, low-power machine. A third option, 128GB AMD Ryzen AI Max+ 395 mini PCs, sits between the two. This guide breaks down memory, bandwidth, speed ceilings, software and cost so you can decide with real numbers.
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Mac vs PC for Local LLMs: The Quick Verdict
| Your Situation | Better Choice | Why |
|---|---|---|
| Mostly 7B to 14B models, want speed per dollar | PC (RTX 5070 Ti or 5080) | 16GB VRAM at 896 to 960 GB/s runs small models very fast |
| 32B coding or reasoning models at full speed | PC (RTX 5090) | 32GB VRAM at 1,792 GB/s fits 32B at 4-bit with room for context |
| 70B dense models on one machine | Mac (64GB to 128GB) or 128GB Strix Halo | A 70B model at 4-bit needs about 42GB plus context |
| Large mixture-of-experts models (100B+) | Mac Studio 128GB or 128GB Strix Halo | Capacity matters most; only active experts are read per token |
| Fine-tuning and training | PC (NVIDIA) | CUDA has the broadest training library support |
| Silent desk machine, low power bill | Mac | Apple Silicon draws a fraction of a high-end GPU’s power |
| Also want to game | PC | RTX 50-series cards double as flagship gaming GPUs |
How It Works: Unified Memory vs Dedicated VRAM
Apple Silicon: one big shared pool
On an M-series Mac, the CPU, GPU and Neural Engine all read from the same unified memory. There is no separate VRAM, so when you buy a Mac Studio with 128GB, the GPU can use most of that memory for model weights. macOS reserves part of it for the system, so plan on a little less than the full figure being available to the model.
The 2026 lineup gives three memory tiers. The Mac mini with the base M6 chip comes with 16GB and tops out at 32GB, at 170GB/s. The Mac mini with M5 Pro offers 24GB, 48GB or 64GB at 307GB/s. The Mac Studio with M5 Max starts at 36GB and goes to 48GB, 64GB or 128GB, with up to 614GB/s, and the M5 Ultra Mac Studio scales to 512GB at up to 1.2TB/s. Memory is soldered on every model, so the amount you buy is permanent.
NVIDIA PC: small, very fast VRAM
A gaming-style PC keeps model weights in the graphics card’s own GDDR7 memory. The RTX 5070 Ti and RTX 5080 have 16GB, and the RTX 5090 has 32GB. That memory is much faster than any unified pool: 896GB/s, 960GB/s and 1,792GB/s respectively. If a model is too big for VRAM, tools like llama.cpp can split layers between the GPU and system RAM, but the part in system RAM runs at DDR5 speed, and overall generation slows sharply.
The upside of the PC route is flexibility. You can add system RAM, swap the GPU in a later generation, or add a second card. The downside is that the largest single consumer GPU still tops out at 32GB.
Hardware Compared: Memory, Bandwidth and Power
| Machine or GPU | Memory for Models | Memory Bandwidth | Power Note | Upgradeable? |
|---|---|---|---|---|
| Mac mini (M6) | 16GB to 32GB unified | 170 GB/s | Very low | No |
| Mac mini (M5 Pro) | 24GB, 48GB or 64GB unified | 307 GB/s | Low | No |
| Mac Studio (M5 Max) | 36GB to 128GB unified | Up to 614 GB/s | Low to moderate | No |
| Mac Studio (M5 Ultra) | 96GB to 512GB unified | Up to 1.2 TB/s | Moderate | No |
| PC with RTX 5070 Ti | 16GB GDDR7 + system RAM | 896 GB/s | 300W GPU board power | Yes |
| PC with RTX 5080 | 16GB GDDR7 + system RAM | 960 GB/s | 360W GPU board power | Yes |
| PC with RTX 5090 | 32GB GDDR7 + system RAM | 1,792 GB/s | 575W GPU board power | Yes |
| Ryzen AI Max+ 395 mini PC | 128GB (up to 96GB as VRAM on Windows) | 256 GB/s | About 120W class (AMD Halo) | No |
| NVIDIA DGX Spark (GB10) | 128GB unified | 273 GB/s | Low to moderate | No |
Speed: What Bandwidth Means for Tokens per Second
When an LLM generates text, it reads essentially all of its active weights from memory for every token. That makes memory bandwidth the main speed limit for single-user chat. A simple rule gives the theoretical ceiling:
Max tokens per second ≈ memory bandwidth (GB/s) ÷ model size in memory (GB)
The table applies that formula to three common 4-bit (Q4_K_M) model sizes: an 8B model at about 4.9GB, a 32B model at about 20GB and a 70B model at about 42.5GB. These are calculated upper bounds, not measurements. Real-world generation is lower because of compute limits, software efficiency, context length and overhead, but the ranking between machines holds up well.
| Hardware | Bandwidth | 8B Q4 Ceiling | 32B Q4 Ceiling | 70B Q4 Ceiling |
|---|---|---|---|---|
| Mac mini (M6, 32GB) | 170 GB/s | About 35 tok/s | About 8 tok/s (tight fit) | Does not fit |
| Mac mini (M5 Pro, 48GB to 64GB) | 307 GB/s | About 63 tok/s | About 15 tok/s | About 7 tok/s (64GB only) |
| Mac Studio (M5 Max, 64GB+) | 614 GB/s | About 125 tok/s | About 31 tok/s | About 14 tok/s |
| Mac Studio (M5 Ultra) | 1,200 GB/s | About 245 tok/s | About 60 tok/s | About 28 tok/s |
| RTX 5070 Ti (16GB) | 896 GB/s | About 183 tok/s | Does not fit in VRAM | Does not fit in VRAM |
| RTX 5090 (32GB) | 1,792 GB/s | About 366 tok/s | About 90 tok/s | Does not fit in VRAM |
| Ryzen AI Max+ 395 (128GB) | 256 GB/s | About 52 tok/s | About 13 tok/s | About 6 tok/s |
| DGX Spark (128GB) | 273 GB/s | About 56 tok/s | About 14 tok/s | About 6 tok/s |
Three things stand out. First, on any model that fits in VRAM, an RTX 5090 is roughly three times faster than an M5 Max and about seven times faster than a 128GB Strix Halo box. Second, only the Macs, the Strix Halo systems and DGX Spark can hold a 70B model at 4-bit on one device. Third, the M5 Max is the fastest way to run 70B in a single quiet box short of the far pricier M5 Ultra.
Prompt processing is a different story
The table covers generation speed. Reading a long prompt (prefill) is limited by raw compute instead of bandwidth, and that is where NVIDIA GPUs pull further ahead thanks to their tensor cores. If you paste whole codebases or long documents into a model, a PC will start answering sooner. Apple added Neural Accelerators to every GPU core in the M5 family to narrow this gap, but a high-end RTX card remains stronger at prefill.
Mixture-of-experts models change the math
MoE models only read a fraction of their weights per token. A 120B-class MoE model with around 5B active parameters needs about 60GB to 65GB of memory to load, but generates text at speeds closer to a much smaller dense model. That is why 128GB Macs and Strix Halo machines have become popular for running large open-weight MoE models: capacity gets the model loaded, and the small active size keeps it responsive.
Software: CUDA vs Metal and MLX
PC with NVIDIA (CUDA)
CUDA remains the default target for AI software. Ollama, LM Studio and llama.cpp all run well, and you also get vLLM and TensorRT-LLM for high-throughput serving, plus PyTorch, Hugging Face training tools, Unsloth-style fine-tuning and ComfyUI for image and video models. If a new research project ships code, it almost always runs on NVIDIA first. Windows support has also improved: in October 2026 Microsoft announced llama.cpp support inside Windows ML, and both Windows and Linux work well with NVIDIA drivers.
Mac (Metal and MLX)
On macOS, Ollama, LM Studio and llama.cpp use Metal for GPU acceleration, and Apple’s MLX framework is fast for both inference and small fine-tunes. LM Studio can run models through its MLX backend, which often outperforms the GGUF path on the same Mac. The setup is simple: install an app, download a model, chat. What you give up is CUDA. vLLM support on Mac is limited, many training repositories assume NVIDIA, and multi-GPU scaling is not an option.
The AMD middle ground
Ryzen AI Max+ 395 systems run x86 Windows or Linux, with ROCm and Vulkan backends in llama.cpp and LM Studio. Software support is broader than it was a year ago but still behind CUDA for training. For inference with popular apps, they work well.
Best Mac Options for Local LLMs
Mac Studio (M5 Max): the capacity pick
Key specs: M5 Max, 18-core CPU, 40-core GPU | 48GB unified memory in this configuration (up to 128GB available) | up to 614GB/s | Thunderbolt 5
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The M5 Max Mac Studio, shipping since September 2026, is the most balanced Mac for local AI. A 48GB configuration runs 32B models at 4-bit or 8-bit with plenty of context headroom. If 70B models are the goal, step up to 64GB or 128GB at purchase; the 128GB option is tied to the 40-core GPU. Expect a calculated ceiling around 31 tokens per second on a 32B Q4 model, which is comfortable for chat and coding help.
Mac mini (M5 Pro): the compact value pick
Key specs: M5 Pro, 15-core CPU and 16-core GPU base (up to 18-core CPU and 20-core GPU) | 24GB, 48GB or 64GB | 307GB/s | three Thunderbolt 5 ports, 10Gb Ethernet option
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The M5 Pro Mac mini doubles the bandwidth of the base M6 model, which matters more than any other spec for LLM speed. The 24GB base is fine for 8B to 14B models; the 48GB configuration is the sweet spot for 32B models. A 64GB build can hold a 70B model at 4-bit, but generation will be slow at about 7 tokens per second as a theoretical ceiling.
Best PC Options for Local LLMs
RTX 5090 desktop: the speed pick
Key specs: Ryzen 9 9950X3D (16 cores) | RTX 5090 32GB GDDR7 | 32GB DDR5 | 2TB SSD
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An RTX 5090 system is the fastest single-GPU option for local LLMs, with 32GB of VRAM at 1,792GB/s. It runs 32B models at 4-bit entirely on the GPU and handles image and video generation at the same time. Plan to add RAM to reach 64GB for offloading and multitasking, and budget for the card’s 575W power draw. RTX 5090 pricing has run far above launch levels during the 2026 memory shortage, so this is firmly a premium-tier choice.
RTX 5070 Ti desktop: the value pick
Key specs: Ryzen 7 9700X | RTX 5070 Ti 16GB GDDR7 | 32GB DDR5 | 1TB NVMe + 1TB SATA SSD
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For 7B to 14B models, an RTX 5070 Ti PC delivers speed that no Mac in its price tier can match, with a calculated ceiling of about 183 tokens per second on an 8B Q4 model. It also gives you full CUDA support for experimenting with fine-tuning. The 16GB limit is the trade-off: 32B models will not fit without offloading.
The Middle Ground: 128GB Ryzen AI Max+ 395 Mini PCs
Key specs (GMKtec X3): Ryzen AI Max+ 395 | 128GB LPDDR5X | 2TB PCIe 4.0 SSD
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If you want Mac-like capacity on Windows or Linux, AMD’s Ryzen AI Max+ 395 is the chip to look at. With 128GB of LPDDR5X at 256GB/s and up to 96GB assignable as graphics memory on Windows, these mini PCs load 70B models and large MoE models like a high-memory Mac. Bandwidth is less than half of an M5 Max, so expect slower generation on dense models. The appeal is getting 128GB on an x86 platform you can run Windows or Linux on. NVIDIA’s DGX Spark offers the same 128GB capacity with CUDA at 273GB/s, but its price rose sharply in 2026.
Cost: What Each Budget Tier Buys
| Price Tier | Mac Option | PC Option | Largest Comfortable Model (Q4) |
|---|---|---|---|
| Entry | Mac mini M6, 24GB to 32GB | Desktop with 16GB GPU (used or previous-gen) | 8B to 14B |
| Mid-range | Mac mini M5 Pro, 48GB | RTX 5070 Ti desktop | Mac: 32B (slower) / PC: 14B to 20B (fast) |
| Upper-mid | Mac Studio M5 Max, 48GB to 64GB | RTX 5080 desktop or 128GB Strix Halo mini PC | Mac: 32B to 70B / PC: 14B to 20B or 70B (Strix Halo) |
| Premium | Mac Studio M5 Max, 128GB | RTX 5090 desktop | Mac: 70B and 120B MoE / PC: 32B (fastest) |
| Flagship | Mac Studio M5 Ultra, 256GB to 512GB | Dual-GPU or workstation GPU builds | 200B+ class models |
Two cost notes for 2026. Apple raised Mac mini base prices by about $100 this year amid the memory shortage, and RTX 5090 cards have sold far above their launch price. Factor in electricity too: a GPU tower under sustained load draws several hundred watts, while a Mac doing the same job draws much less.
Who Should Buy a Mac for Local LLMs?
- You want to run 70B dense models or 100B+ MoE models on a single machine.
- You value silence, low heat and low power draw, for example in a home office.
- You mainly do inference: chat, coding assistance, RAG over your own documents.
- You prefer a simple setup with apps like LM Studio and Ollama.
- You already work in macOS and want one machine for everything.
Who Should Build or Buy a GPU PC?
- You mostly run 7B to 32B models and want the highest tokens per second for your money.
- You plan to fine-tune, train LoRAs or use vLLM and other CUDA-first tools.
- You process long prompts often and want faster prefill.
- You want to upgrade the GPU or add RAM over time.
- You also want a high-end gaming machine.
Decision Checklist
- Write down the largest model you want to run. If it is 32B or smaller, a PC is usually faster for the money. If it is 70B or a large MoE, look at 64GB+ Macs or 128GB Strix Halo.
- Estimate memory needed. At 4-bit, budget about 0.6GB per billion parameters plus 10% to 30% for context.
- Check bandwidth. Divide bandwidth by model size to estimate the speed ceiling. Under about 10 tokens per second feels slow for chat.
- Decide on training. If yes, choose NVIDIA.
- Buy memory for the future. Mac memory is permanent, so size up; on a PC, plan the GPU first and add RAM later.
FAQ
Is a Mac or a PC better for running LLMs locally?
It depends on model size. A PC with an NVIDIA RTX GPU is faster for models that fit in its 16GB to 32GB of VRAM and has better software support for training. A Mac with 64GB to 128GB of unified memory can run much larger models, such as 70B, on one quiet machine.
Why can a Mac run larger models than a gaming PC?
Because Apple Silicon uses unified memory shared by the CPU and GPU. A Mac Studio with 128GB lets the GPU use most of that pool for model weights, while the largest consumer NVIDIA card, the RTX 5090, has 32GB of VRAM.
How much faster is an RTX 5090 than a Mac Studio for LLMs?
On models that fit in 32GB, the RTX 5090’s 1,792GB/s of bandwidth gives it a theoretical generation ceiling roughly three times higher than an M5 Max at 614GB/s. It is also faster at processing long prompts. On 70B models, the 5090 cannot hold the model in VRAM, so the Mac wins.
Can a Mac mini run a 70B model?
Only the M5 Pro Mac mini with 64GB can hold a 70B model at 4-bit (about 42GB plus context). With 307GB/s of bandwidth, the theoretical ceiling is around 7 tokens per second, which is usable but slow. A Mac Studio with M5 Max is a better 70B machine.
Is 16GB of VRAM enough for local LLMs?
Yes for 7B to 14B models, and up to roughly 20B at 4-bit. A 32B model at 4-bit is about 20GB, so it needs a 24GB to 32GB GPU or a unified-memory system. For 2026, 16GB is the practical minimum for a local AI GPU.
Is a Ryzen AI Max+ 395 mini PC a good alternative to a Mac?
For capacity, yes. These systems offer 128GB of memory, with up to 96GB usable as graphics memory on Windows, and run x86 Windows or Linux. Their 256GB/s bandwidth is lower than an M5 Max, so generation on big dense models is slower.
Can I fine-tune models on a Mac?
You can fine-tune small models with Apple’s MLX framework, and it works well for LoRA-style adapters. For larger fine-tunes and most training libraries, an NVIDIA GPU with CUDA is the more practical choice.
Bottom Line
For most people running 7B to 32B models, a PC with an RTX 5070 Ti, 5080 or 5090 gives the most speed per dollar and the widest software support. For anyone whose target is 70B or larger, a Mac Studio with M5 Max and 64GB to 128GB, or a 128GB Ryzen AI Max+ 395 mini PC, is the simpler path to running those models on one device.
Sources
- Apple Newsroom: Mac Studio (M5 Max, M5 Ultra) and Mac mini (M6, M5 Pro) announcements, August 2026
- NVIDIA: GeForce RTX 5090, 5080 and 5070 Ti specifications; DGX Spark product information
- AMD: Ryzen AI Max+ 395 and Ryzen AI Halo Developer Platform specifications
- Microsoft: Windows ML and llama.cpp support announcement, October 2026
Last updated: October 10, 2026
