On a 64 GB Mac, 12 catalog models run fully on the GPU at an 8,192-token context. The most capable is Hunyuan-A13B-Instruct at IQ4_XS (needs ~46.2 GiB). Pick a smaller model or a lower quant for more headroom.
Figures assume a 64 GB unified-memory Mac (one pool shared by CPU and GPU), judged at an 8,192-token context. Speeds depend on the specific chip — see the GPU pages.
TL;DR
A 64 GB Mac is where the dense 70B class first fits at 4-bit — but it fits at the top of the budget, not with margin. macOS hands the GPU only part of the unified pool by default — roughly three quarters, and about two thirds on machines at 32 GB and below, so a 64 GB machine budgets against about 48, and a 70B at a 4-bit sweet-spot quant lands close to all of it before you have asked for much context. The comfortable models at this tier are the ones a class below. The list below is derived live from the fit engine, largest first.
Everything below the biggest entries fits with real slack: the 30B class runs at near-lossless quants with a full desktop open, and this is the first tier where running more than one model at once — a chat model alongside an embedder and reranker for RAG — is an everyday setup rather than a stunt.
The top entries deserve respect. They draw from the same pool as macOS and your apps, and they leave little room for the KV cache, which is the thing that grows when you actually use a long context. Load them on a quiet machine and check the calculator at the context you really want.
Hunyuan-A13B-Instruct at IQ4_XS · 80.4B params · needs ~46.2 GiB
One command to run it (llama.cpp):
llama-server -m tencent_Hunyuan-A13B-Instruct-IQ4_XS.gguf -c 8192 -ngl 999| Model | Sweet-spot quant | Fits in |
|---|---|---|
| Hunyuan-A13B-Instruct tencent | IQ4_XS Runs fully on GPU | ~46.2 GiB |
| Kimi-Dev-72B moonshotai | IQ4_XS Runs fully on GPU | ~43.8 GiB |
| Qwen3.6-35B-A3B Qwen | Q8_0 Runs fully on GPU | ~39 GiB |
| Qwen3-Omni-30B-A3B-Instruct Qwen | Q4_K_M Runs fully on GPU | ~20.3 GiB |
| Qwen2.5-VL-32B-Instruct Qwen | Q8_0 Runs fully on GPU | ~38.3 GiB |
| DeepSeek-R1-Distill-Qwen-32B deepseek-ai | Q8_0 Runs fully on GPU | ~38.3 GiB |
| Qwen3-32B Qwen | Q8_0 Runs fully on GPU | ~38.3 GiB |
| gemma-4-31B-it google | Q8_0 Runs fully on GPU | ~36 GiB |
| GLM-4.7-Flash zai-org | Q8_0 Runs fully on GPU | ~33.6 GiB |
| Qwen3-Coder-30B-A3B-Instruct Qwen | Q8_0 Runs fully on GPU | ~34.6 GiB |
| Qwen3-30B-A3B-Instruct-2507 Qwen | Q8_0 Runs fully on GPU | ~34.6 GiB |
| Qwen3.6-27B Qwen | Q8_0 Runs fully on GPU | ~31.9 GiB |
Derived live from the fit engine + catalog at an 8,192-token context. "Fits in" is the modelled VRAM the sweet-spot quant needs (weights + KV cache + overhead). Speed depends on your specific card — check a GPU page or the calculator.
It is where the dense 70B class first fits at all, at a 4-bit sweet-spot quant — but it fits near the top of what the GPU is allowed to claim, so it is a tight fit rather than a comfortable one, and long context is where you'll feel that. The broader gain is the same as always one tier up: the models a class below run at healthier quants with room to keep working while they're loaded.
Yes, within reason — a mid-size chat model plus an embedding model and reranker for RAG is a realistic combination at this tier. Each model holds its own weights and KV cache from the same shared pool, so add up the footprints in the fit calculator rather than assuming the leftovers will cover it.
They win at different things. The Mac's unified pool fits model classes that consumer cards generally can't hold at all, which is the whole appeal at this tier. But generation speed tracks memory bandwidth, and high-end discrete GPUs are typically faster on the models that do fit them. Buy the Mac path for fit and quiet convenience, the GPU path for raw speed on smaller classes.
Yes — the KV cache grows with context and grows fastest on the large models this tier invites you to run. The verdicts here hold at the stated context; if very long context is the point of your upgrade, verify the exact model, quant, and context in the fit calculator before downloading.
This page ranks by fit, not speed. A 64 GB configuration exists across several Apple Silicon tiers, and generation speed mostly tracks the chip's memory bandwidth — large dense models feel that difference most. Check your specific chip's page for speed context.
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