By LLM Hardware team · Methodology ·
AI structured the comparison. The "when Mac wins / when PC wins" verdicts are editorial calls based on the published specs and community benchmarks cited on this page.
Checked 2026-10-13. Author: LLM Hardware team. This site does not store or show Amazon prices. Check current price on Amazon.
Key fact: A 24 GB discrete GPU and a 24 GB Mac are not the same fit. The GPU ceilings near 36B at Q4. The Mac subtracts 4 GB for macOS first.
How we got it: floor((24 − 2) / 0.6) = 36. floor((24 − 4 − 2) / 0.6) = 30. The 4 GB reserve is the site’s “several GB” / “4–5 GB” unified-memory assumption, on top of the formula’s +2.
Checked:
Key fact: Llama 3.1 70B at Q4 is 44 GB. It does not fit a 32 GB GPU or a 36 GB Mac Studio after the OS reserve.
How we got it: 70 × 0.6 + 2 = 44. A 36 GB Mac usable pool is about 32 GB before the formula reserve. ollama run llama3.1:70b and llama3.3:70b were on the library on 7 October 2026.
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Methodology — parameter math, quantization bytes, and the source list.
Updated 24 October 2026 · Fit uses params × 0.6 + 2 · Mac prices were not reconfirmed (buy box not readable)
Amazon’s clean Mac buy boxes on 2026-10-01 were the Mac Studio M5 Max 36GB, the Mac mini M5 Pro 24GB, and the Mac mini M4 Pro 24GB. The 48GB M4 Pro and 64GB M4 Max Studio listings were unavailable. 64GB+ and Ultra are configured at Apple. The ranked list, prices, and model tiers are in the Apple Silicon guide.
Mac vs PC is the most debated question in the local LLM community. The answer is not one-size-fits-all: it depends entirely on which model sizes you want to run. Apple Silicon's unified memory architecture removes the hard VRAM ceiling that limits GPU PCs — but GPU PCs are faster and cheaper for models up to 34B. This guide maps out exactly when each platform wins.
| Use case | Best pick | Why |
|---|---|---|
| 7B models (Llama 3.1 8B, Mistral 7B) | RTX 4060 PC (GPU) | 8 GB fits 8B at Q4 (6.8 GB); lowest-cost CUDA option |
| 14B models at Q4 (budget) | RTX 4060 Ti 16GB PC (GPU) | 16 GB fits 14B at Q4 (10.4 GB). 13B at Q8 needs 17.6 GB and does not fit 16 GB |
| 14B models at Q4 (faster) | RTX 4070 Ti Super 16GB PC (GPU) | 672 GB/s vs 288 GB/s on the 4060 Ti 16GB (NVIDIA specs), same 16 GB |
| 27–32B Q4 on a Mac (in stock Oct 2026) | Mac Studio M5 Max 36GB | 24GB minis are tight at 27B. Check current price on Amazon. |
| 7–27B Q4, compact Mac | Mac mini M5 Pro or M4 Pro 24GB | B0HGGHNQY6 or B0DLBVHSLD. 48GB M4 Pro was unavailable. Check current price on Amazon. |
| Large MoE / DeepSeek-V4-Flash quant | EVO-X2 128GB or Apple Ultra | Beelink GTR9 Pro is the same silicon with a NIC caveat, not the default. 64GB+ Studio and Ultra configure at Apple. Check current price on Amazon. |
| 64GB+ Mac Studio | Apple configure, not Amazon | M4 Max 64GB (B0FNS1ZX5B) was unavailable on 2026-10-01. Order 64GB M5 Max or Ultra at apple.com. |
| Low power / silent operation | Any Apple Silicon Mac | Mac uses 25–30W idle vs 350–450W for a full GPU PC |
| Best bang for the buck overall | RTX 4060 Ti 16GB PC | More VRAM than RTX 4070 for less money — 16 GB at a budget price |
The core difference between a Mac and a GPU PC for LLM inference is how memory is organized.
Apple Silicon — Unified Memory
The CPU, GPU, and Neural Engine share a single physical memory pool on the same die. A Mac Studio with 128 GB gives every compute unit access to all 128 GB at 400–800 GB/s bandwidth — no copying, no PCIe bottleneck.
NVIDIA GPU PC — Dedicated VRAM
The GPU has its own VRAM chip separate from system RAM. The RTX 4090 has 24 GB GDDR6X — fast, but fixed. System RAM exists separately and reaching it requires the PCIe bus (max ~64 GB/s), which causes severe inference slowdowns if the model spills out of VRAM.
The key insight: A 70B model at Q4_K_M quantization needs ~37–40 GB of fast memory. The RTX 4090's 24 GB VRAM cannot fit it. The Mac Studio M4 Max's 64 GB unified memory can — and accesses it at 400+ GB/s rather than the ~64 GB/s PCIe limit. This is why Apple Silicon is the go-to for 70B.
Approximate generation throughput at Q4_K_M. OOM = out of memory (cannot run). * = requires CPU offloading.
| Model size | RTX 4060 | RTX 4070 | RTX 4090 | RTX 5090 | Mac mini M4 16GB | Mac mini M4 Pro 48GB | Mac Studio M4 Max 64GB |
|---|---|---|---|---|---|---|---|
| 7B at Q4_K_M | ~35 | ~50 | ~60 | ~70 | ~25 | ~30 | ~35 |
| 13B at Q4_K_M | OOM | ~30 | ~45 | ~55 | OOM | ~22 | ~28 |
| 34B at Q4_K_M | OOM | OOM | ~25 | ~32 | OOM | ~14 | ~18 |
| 70B at Q4_K_M | OOM | OOM | OOM | OOM* | OOM | OOM | ~10 |
Actual speeds vary by system RAM speed, background load, and context length. See GPU guide and Apple Silicon guide for per-device details.
GPU PCs win on price-per-token for small to mid models. Macs justify their cost only at 70B+, where GPU PCs simply cannot compete on a single device.
Budget tier — PC wins clearly
Lowest costRTX 4060 (GPU + PC)
8 GB fits 8B at Q4 (6.8 GB). Speed not measured here. CUDA ecosystem. GPU can be upgraded later.
Mac mini M4 16GB
16 GB unified memory fits 8B at Q4 (6.8 GB). Speed not measured here. Cannot upgrade, but no driver setup.
Mid tier — PC still leads on speed
Speed advantage: PCRTX 4070 (GPU + PC)
12 GB fits 14B at Q4 (10.4 GB) with little context headroom. Speed not measured here.
Mac mini M4 Pro 48GB
The memory class fits 27–36B at Q4. Amazon listings B0DS2XP86K and B0DYFCQ7CB were unavailable on 2026-10-01, so this is not a current buy. The in-stock substitute is the 24GB Pro mini or the 36GB M5 Max Studio.
High tier — PC faster for 34B, Mac needed for 70B
Inflection pointRTX 4090 (GPU + PC)
24 GB fits 32B at Q4 (21.2 GB). 127.7 tok/s on Llama 3 8B Q4_K_M (XiongjieDai, May 2024). 70B at Q4 (44 GB) does not fit.
Mac Studio M4 Max 64GB
ASIN B0FNS1ZX5B had no featured offer on 2026-10-01. The 64GB class is an Apple configure until it returns. The in-stock Studio that day was the M5 Max 36GB.
Annual energy estimated at the load figure for 8 hours/day. Wattages are editorial estimates of full-system draw, not measurements. Multiply by your local rate.
| Device | Idle | Under LLM load | Est. annual energy |
|---|---|---|---|
| Mac mini M4 (16GB) | ~6W | ~25W | ~73 kWh |
| Mac mini M4 Pro (48GB) | ~8W | ~35W | ~102 kWh |
| Mac Studio M4 Max (64GB) | ~18W | ~120W | ~350 kWh |
| RTX 4060 PC | ~50W | ~200W | ~584 kWh |
| RTX 4070 PC | ~55W | ~270W | ~788 kWh |
| RTX 4090 PC | ~65W | ~450W | Check current price on Amazon |
| RTX 5090 PC | ~70W | ~575W | Check current price on Amazon |
Apple Silicon's efficiency advantage is substantial. A Mac mini running LLMs all day costs roughly the same in electricity as leaving a light bulb on. An RTX 4090 PC drawing an estimated 400 W for 8 hours a day uses about 1,170 kWh a year, an ongoing cost that partly offsets the hardware price gap.
NVIDIA PC (CUDA)
The de-facto standard for LLM inference tooling
Apple Silicon (Metal / MLX)
Growing ecosystem, excellent for inference
For pure inference (running models), both platforms are well-supported by Ollama, llama.cpp, and LM Studio. For fine-tuning, training, or serving in production with vLLM, NVIDIA is the clear choice today. See the Apple Silicon guide for MLX details.
ollama pull llama3.1,
done. No CUDA toolkit, no driver version juggling, no BIOS settings.
Do you need to run 70B parameter models?
YES → Mac Studio M4 Max 64GB — only practical single-device option
NO → continue
Do you need to fine-tune models?
YES → GPU PC (any NVIDIA RTX) — CUDA required for Axolotl, unsloth
NO → continue
Is max inference speed your priority?
YES → GPU PC — RTX 4090 was 1.7–2.5x faster than M2 Ultra / M3 Max on Llama 3 8B Q4_K_M (XiongjieDai)
NO → continue
Do you value silence and low power above speed?
YES → Mac mini M4 or M4 Pro — 25–35W, completely silent
NO → GPU PC — better value for pure inference
It depends on model size. For 7B–34B models, a GPU PC is faster and cheaper. For 70B models, the Mac Studio M4 Max 64 GB is the only practical single-device consumer option. Macs also use far less power and require zero driver management.
Apple Silicon uses unified memory: the CPU and GPU share a single physical RAM pool. A Mac Studio with 128 GB gives every compute unit access to all 128 GB at 400–800 GB/s. An NVIDIA RTX 4090 has a hard 24 GB VRAM ceiling — if the model exceeds that, it spills into system RAM across the PCIe bus at ~64 GB/s, causing severe slowdowns. This is why 70B models only run well on Apple Silicon or multi-GPU setups.
We have not measured 13B. The closest public data is Llama 3 8B Q4_K_M in the XiongjieDai llama.cpp runs (May 2024): RTX 4090 127.7 tok/s, M2 Ultra 76-core 76.3 tok/s, M3 Max 40-core 50.7 tok/s. That is about 1.7–2.5x in favour of the GPU. M4-generation Macs were not in that set.
Yes. llama.cpp with Metal acceleration runs directly on Apple Silicon with no wrapper needed. MLX (Apple's own ML framework) also runs LLMs natively. Ollama and LM Studio are convenience layers on top of llama.cpp — useful but optional.
If 70B models are your target, the Mac Studio M4 Max 64 GB is the most practical single-device option. The alternative — dual RTX 4090s — is expensive in GPUs alone, requires a compatible motherboard, higher power draw, and tensor-splitting configuration. For users who only need 7B–34B models, a GPU PC is faster and cheaper.
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VRAM figures on this page come from the site’s VRAM formula and model cards. The sitewide formula and the full source list are on the methodology page. For this guide we used:
Spot a number that does not match the linked source? Contact us and we will update the guide.