LocalLM Fit

Mac configuration

MacBook Pro M4 Pro · 64 GB

64 GB unified memory · 273 GB/s memory bandwidth · verified

Unified memory64 GB
Memory bandwidth273 GB/s
BackendsOllama, llama.cpp, MLX

The largest M4 Pro memory option. Capacity is high, but M4 Max configurations offer higher memory bandwidth for generation speed.

What runs comfortably at 8K

ModelQuantizationRequirementContext ceilingMLX commandOllama command
Qwen3.6 35B-A3B35B total / 3B active Q8_0 39.5 GB 262,144 tokens mlx_lm.generate --model Qwen/Qwen3.6-35B-A3B --prompt "Your prompt" --max-tokens 8192 ollama run qwen3.6:35b-a3b-q8_0
Gemma 4 31B30.7B dense Q8_0 36.5 GB 199,945 tokens mlx_lm.generate --model google/gemma-4-31B-it --prompt "Your prompt" --max-tokens 8192 ollama run gemma4:31b-it-q8_0
Qwen3.8 27B27B dense Q8_0 32.0 GB 262,144 tokens mlx_lm.generate --model Qwen/Qwen3.8-27B --prompt "Your prompt" --max-tokens 8192 ollama run qwen3.8:27b-q8_0
gpt-oss-20b21B total / 3.6B active MXFP4 13.8 GB 131,072 tokens MLX package not verified ollama run gpt-oss:20b
Gemma 4 12B Unified11.95B dense Q8_0 14.7 GB 262,144 tokens mlx_lm.generate --model google/gemma-4-12B-it --prompt "Your prompt" --max-tokens 8192 ollama run gemma4:12b-it-q8_0
Qwen3 8B8.2B dense Q8_0 11.6 GB 32,768 tokens MLX package not verified ollama run qwen3:8b-q8_0
Gemma 4 E4B4.5B effective / 8B stored Q8_0 11.0 GB 131,072 tokens mlx_lm.generate --model google/gemma-4-E4B-it --prompt "Your prompt" --max-tokens 8192 ollama run gemma4:e4b-it-q8_0
DeepSeek R1 8B8B dense Q8_0 11.1 GB 131,072 tokens MLX package not verified ollama run deepseek-r1:8b-llama-distill-q8_0
Mistral 7B7.2B dense Q8_0 10.3 GB 32,768 tokens MLX package not verified ollama run mistral:7b-instruct-v0.3-q8_0
Gemma 3 4B4B dense Q8_0 7.6 GB 131,072 tokens MLX package not verified ollama run gemma3:4b-it-q8_0
Phi-4 Mini3.8B dense Q8_0 6.7 GB 131,072 tokens MLX package not verified ollama run phi4-mini:3.8b-q8_0
Llama 3.2 3B3.2B dense Q8_0 5.8 GB 131,072 tokens MLX package not verified ollama run llama3.2:3b-instruct-q8_0

Tight fits and limits

These models are estimated to fit, but with less than 20% headroom. They are not the best first purchase target.

No model lands in the tight-only band.

Quantization trade-offs for the five featured models

The same 8K formula is applied to each published quantization. A lower-bit file can fit while a higher-quality file does not; that is a capacity result, not a promise that every quantization has the same output quality or backend support.

ModelQuantization8K requirementFitContext ceilingOllama / LM Studio
gpt-oss-20b MXFP4 13.8 GB Comfortable fit 131,072 tokens ollama run gpt-oss:20blms get "gpt-oss-20b"@mxfp4
Qwen3.6 35B-A3B MXFP4 24.3 GB Comfortable fit 262,144 tokens ollama run qwen3.6:35b-a3blms get "Qwen3.6 35B-A3B"@mxfp4
Qwen3.6 35B-A3B Q4_K_M 24.7 GB Comfortable fit 262,144 tokens ollama run qwen3.6:35b-a3b-q4_K_Mlms get "Qwen3.6 35B-A3B"@q4_k_m
Qwen3.6 35B-A3B Q8_0 39.5 GB Comfortable fit 262,144 tokens ollama run qwen3.6:35b-a3b-q8_0lms get "Qwen3.6 35B-A3B"@q8_0
Qwen3.8 27B Q4_K_M 19.4 GB Comfortable fit 262,144 tokens ollama run qwen3.8:27b-q4_K_Mlms get "Qwen3.8 27B"@q4_k_m
Qwen3.8 27B Q8_0 32.0 GB Comfortable fit 262,144 tokens ollama run qwen3.8:27b-q8_0lms get "Qwen3.8 27B"@q8_0
Gemma 4 12B Unified Q4_0 9.1 GB Comfortable fit 262,144 tokens ollama run gemma4:12b-it-qatlms get "Gemma 4 12B Unified"@q4_0
Gemma 4 12B Unified Q4_K_M 9.2 GB Comfortable fit 262,144 tokens ollama run gemma4:12b-it-q4_K_Mlms get "Gemma 4 12B Unified"@q4_k_m
Gemma 4 12B Unified Q8_0 14.7 GB Comfortable fit 262,144 tokens ollama run gemma4:12b-it-q8_0lms get "Gemma 4 12B Unified"@q8_0
Gemma 4 E4B Q4_K_M 7.7 GB Comfortable fit 131,072 tokens ollama run gemma4:e4b-it-q4_K_Mlms get "Gemma 4 E4B"@q4_k_m
Gemma 4 E4B Q4_0 7.9 GB Comfortable fit 131,072 tokens ollama run gemma4:e4b-it-qatlms get "Gemma 4 E4B"@q4_0
Gemma 4 E4B Q8_0 11.0 GB Comfortable fit 131,072 tokens ollama run gemma4:e4b-it-q8_0lms get "Gemma 4 E4B"@q8_0

Why the unified-memory number is not all yours

The calculator compares the model requirement with the configuration's unified memory, but macOS and the GPU driver reserve memory before the model starts. Closing other applications and reducing the context is more important on a 16 GB Mac than on a dedicated 24 GB GPU with its own VRAM.

The speed band is relative, not a measured tokens-per-second result. The model's active parameter count and the chip's 273 GB/s memory bandwidth are useful for comparison, but backend kernels and thermals still change the outcome.

Low-cost setup upgrades

Accessories that make MacBook Pro M4 Pro · 64 GB easier to live with

These optional upgrades improve storage, connectivity, or power resilience. None increases unified memory or turns an oversized model into a fit.

Mac mini dock or stand with an NVMe slot

Adds storage and front-facing ports without buying a larger internal drive. Verify the Mac model, Thunderbolt version, and power-delivery limits before ordering.

Search current listings on Amazon

External NVMe SSD for model storage

Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.

Search current listings on Amazon

Thunderbolt or USB4 NVMe enclosure

A reusable enclosure lets you choose the drive capacity separately. Confirm the host supports the advertised Thunderbolt or USB4 generation.

Search current listings on Amazon

As an Amazon Associate, LocalLM Fit earns from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.

If the model does not fit

Start by reducing context and choosing the lowest published quantization. The next choices are a smaller model or a Mac configuration with more unified memory. External SSDs are useful for storing downloads, but they do not add inference memory.

Gemma 4 31B · Q8_0 · 36.5 GB Qwen3.8 27B · Q8_0 · 32.0 GB gpt-oss-20b · MXFP4 · 13.8 GB Gemma 4 12B Unified · Q8_0 · 14.7 GB

Upgrade target

More memory, or more bandwidth?

If your models fit but generation feels slow, prioritize a higher-bandwidth chip tier. If the required model does not fit, prioritize a configuration with more unified memory. Do not treat either number as a live price quote.

Search current listings on Amazon

As an Amazon Associate, LocalLM Fit earns from qualifying purchases. This is a search link, not a live price or availability guarantee. Affiliate disclosure.

Also consider

All Mac configurations All hardware profiles VRAM tier guides Memory formula Apple specification source

FAQ

Mac questions, answered.

How much unified memory can I use for a local model on MacBook Pro M4 Pro · 64 GB?

This profile uses 64 GB of unified memory as the model budget. macOS, the display, browser tabs, and other applications share the same pool, so the actual runtime headroom is lower.

Does memory bandwidth matter on MacBook Pro M4 Pro · 64 GB?

Yes. Apple lists 273 GB/s for this chip tier. Memory capacity decides whether a model can fit; memory bandwidth and GPU compute influence how quickly it generates after it fits.

Why do some pages say MLX is unavailable?

MLX requires a compatible model package and a supported runtime path. If the model record does not list MLX, this guide does not invent a command; use Ollama or llama.cpp after checking the linked source.

Does lower-bit quantization make MacBook Pro M4 Pro · 64 GB faster?

Lower-bit weights reduce capacity pressure and can reduce memory traffic, but this page labels speed as an estimate rather than a benchmark. On MacBook Pro M4 Pro · 64 GB, 273 GB/s memory bandwidth and backend kernel support still matter more than the quantization label alone.

What should I do if a model still does not fit on MacBook Pro M4 Pro · 64 GB?

Reduce context first, then choose a smaller model or a configuration with more unified memory. External storage can hold model files, but it cannot add unified memory. Configurations that fit this machine include Gemma 4 31B, Qwen3.8 27B, gpt-oss-20b, Gemma 4 12B Unified.