LocalLLMChecker

Unified memory · Apple

What AI models can an Apple M4 32GB run?

32 GB puts the 70B class within comfortable reach and leaves room for the context window that makes those models worth running. Memory is shared with the CPU, so the practical ceiling is lower than the sticker number: macOS hands roughly three quarters of it to the GPU by default.

Memory32 GB
Bandwidth120 GB/s
Usable for a model22.4 GB
Runtime backendMETAL

The short answer

Assuming an 8k context window and default settings, these are the models worth downloading first.

Best all-rounder

GPT OSS 20B

MXFP4 · 11.81 GB · about 19.8 tokens/s

Best for code

DeepSeek Coder V2 Lite Instruct

Q8_0 · 16.09 GB · about 16.2 tokens/s

Largest that still runs well

GPT OSS 20B

20.91B parameters · MXFP4 · about 19.8 tokens/s

Fastest useful answer

LFM2.5 8B A1B

about 28.7 tokens/s · 8.78 GB

See how fast it feels

Apple M4 32GB running GPT OSS 20B at MXFP4

Wait for the first word3.1 s
Then writes at19.8 tok/s
Whole answer12 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Where it sits

The GPU can use about 22.4 GB of this machine's 32 GB by default, so for planning it behaves like a 16 GB card with different bandwidth. The 16 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 120 GB/s decides how fast it answers.

Our indicative price for this machine is $999 new, reviewed 2026-09-01. That is 3.2 GB of memory and 12 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.

Devices in a similar price band, fastest memory first:

DevicePriceMemoryBandwidthBest all-round pick
Apple M4 24GB $799 24 GB 120 GB/s GPT OSS 20B, 19.8 t/s

Every model, scored on this device

Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.

Model Params Verdict Download Memory used Speed Max context
GPT OSS 20B
GPT-OSS 		20.91B (4.18B active) Runs well MXFP4 11.81 GB 19.8 t/s 128k
Qwen3.5 9B
Qwen 		9.65B Runs well Q6_K 8.04 GB 11.8 t/s 256k
DeepSeek Coder V2 Lite Instruct
DeepSeek 		15.71B (2.74B active) Runs well Q8_0 16.09 GB 16.2 t/s 128k
Gemma 4 12B Instruct
Gemma 		11.96B Runs well Q4_K_M 8.41 GB 10.2 t/s 64k
Qwen3 8B
Qwen 		8.19B Runs well Q6_K 7.76 GB 11.8 t/s 32k
Gemma 4 E4B Instruct
Gemma 		8B Runs well Q8_0 8.39 GB 10.1 t/s 128k
Qwen3.5 4B
Qwen 		4.66B Runs well Q8_0 5.24 GB 16.6 t/s 256k
Gemma 4 E2B Instruct
Gemma 		5.12B Runs well Q8_0 5.41 GB 15.9 t/s 128k
Granite 4.1 8B
Granite 		8.79B Runs well Q6_K 8.34 GB 10.2 t/s 64k
Qwen3 14B
Qwen 		14.77B Fits, but slow IQ4_XS 8.97 GB 10 t/s 32k
Qwen2.5 VL 7B Instruct
Qwen 		8.29B Runs well Q8_0 9 GB 10.1 t/s 64k
Llama 3.1 8B Instruct
Llama 		8.03B Runs well Q6_K 7.51 GB 12.1 t/s 64k
LFM2.5 8B A1B
Liquid 		8.47B (1.57B active) Runs great Q8_0 8.78 GB 28.7 t/s 64k
Qwen2.5 7B Instruct
Qwen 		7.62B Runs well Q8_0 8.34 GB 11 t/s 32k
Gemma 3 12B Instruct
Gemma 		12.19B Runs well Q4_K_M 8.04 GB 10.6 t/s 128k
Qwen2.5 Coder 7B Instruct
Qwen 		7.62B Runs well Q8_0 8.34 GB 11 t/s 32k
Internlm3 8B Instruct
InternLM 		8.8B Runs well Q6_K 7.47 GB 12.2 t/s 32k
Mistral Nemo Instruct 2407
Mistral 		12.25B Runs well Q4_K_M 8.55 GB 10.5 t/s 64k
Gemma 3 4B Instruct
Gemma 		4.3B Runs well Q8_0 4.88 GB 17.9 t/s 128k
Qwen2.5 14B Instruct
Qwen 		14.77B Runs well Q3_K_M 8.63 GB 10.4 t/s 32k
OLMo 3 7B Instruct
OLMo 		7.3B Runs well Q6_K 8.45 GB 10.5 t/s 64k
Qwen2.5 Coder 14B Instruct
Qwen 		14.77B Runs well Q3_K_M 8.63 GB 10.4 t/s 32k
Qwen3 4B
Qwen 		4.02B Runs well Q8_0 5.43 GB 16 t/s 32k
Mistral 7B Instruct V0.3
Mistral 		7.25B Runs well Q8_0 8.55 GB 10.1 t/s 32k
Gemma 2 9B Instruct
Gemma 		9.24B Runs well Q5_K_M 8.45 GB 10.1 t/s 8k
Phi 4
Phi 		14.66B Runs well Q3_K_M 8.65 GB 10.2 t/s 16k
Mistral 7B Instruct V0.2
Mistral 		7.24B Runs well Q8_0 8.54 GB 10.2 t/s 32k
Granite 4.1 3B
Granite 		3.4B Runs well Q8_0 4.31 GB 20.5 t/s 128k
Qwen2.5 3B Instruct
Qwen 		3.09B Runs well Q8_0 3.65 GB 24.4 t/s 32k
Phi 4 Mini Instruct
Phi 		3.84B Runs well Q8_0 5.14 GB 17 t/s 128k
Show the other 56 models
Llama 3.2 3B Instruct 3.21B Runs well Q8_0 4.39 GB 20.1 t/s 128k
Phi 3 Mini 4k Instruct 3.82B Runs well Q8_0 4.87 GB 18.4 t/s 4k
DeepSeek Coder 7B Instruct V1.5 6.91B Runs well Q5_K_M 8.7 GB 10.1 t/s 4k
LFM2.5 2.6B 2.7B Runs great Q8_0 3.11 GB 29.2 t/s 128k
Qwen3 Next 80B A3B Instruct 81.32B Fits only at 2 to 3 bits IQ2_XXS 20.07 GB 46.3 t/s 64k
Qwen3.5 2B 2.27B Runs great Q8_0 2.67 GB 34.6 t/s 256k
DeepSeek Coder 6.7B Instruct 6.74B Runs well Q4_K_M 8.17 GB 10.6 t/s 16k
Gemma 2 2B Instruct 2.61B Runs great Q8_0 3.51 GB 25.6 t/s 8k
Starcoder2 3B 3.03B Runs great Q8_0 3.46 GB 27.5 t/s 16k
Qwen3 1.7B 2.03B Runs great Q8_0 3.19 GB 28.3 t/s 32k
DeepSeek R1 Distill Qwen 1.5B 1.78B Runs great Q8_0 2.27 GB 46.6 t/s 128k
Qwen2.5 1.5B Instruct 1.54B Runs great Q8_0 2.03 GB 46.8 t/s 32k
SmolLM2 1.7B 1.71B Runs great Q8_0 3.5 GB 25.6 t/s 8k
OLMo 2 0425 1B 1.48B Runs great Q8_0 2.77 GB 36.1 t/s 4k
Llama 3.2 1B Instruct 1.24B Runs great Q8_0 1.79 GB 55.2 t/s 128k
LFM2.5 1.2B Instruct 1.17B Runs great Q8_0 1.56 GB 72.9 t/s 64k
Gemma 3 1B Instruct 1B Runs great Q8_0 1.32 GB 78.5 t/s 32k
Qwen3.5 0.8B 0.87B Runs great Q8_0 1.25 GB 83.9 t/s 256k
Qwen3 0.6B 0.75B Runs great Q8_0 1.89 GB 50.4 t/s 32k
Qwen2.5 0.5B Instruct 0.49B Runs great Q8_0 0.85 GB 141 t/s 32k
LFM2.5 350M 0.35B Runs great Q8_0 0.71 GB 218.3 t/s 64k
Qwen3 Coder Next 79.67B Fits only at 2 to 3 bits n/a 28.95 GB 34 t/s n/a
Qwen3.5 35B A3B 35.95B Just fits n/a 27.98 GB 19.7 t/s n/a
GLM 4.7 Flash 31.22B Just fits n/a 24.56 GB 14.9 t/s n/a
Qwen3 30B A3B 30.53B Just fits n/a 24.37 GB 14.4 t/s n/a
Qwen3 Coder 30B A3B Instruct 30.53B Just fits n/a 24.37 GB 14.4 t/s n/a
Gemma 4 26B A4B Instruct 25.81B Just fits n/a 26.69 GB 10.5 t/s n/a
GLM 4.5 Air 110.47B Fits only at 2 to 3 bits n/a 28.31 GB 10.2 t/s n/a
Phi 3.5 MoE Instruct 41.87B Just fits n/a 24.92 GB 10 t/s n/a
Mixtral 8x7B Instruct V0.1 46.7B Just fits n/a 27.63 GB 5.8 t/s n/a
Codestral 22B V0.1 22.25B Just fits n/a 24.21 GB 3.5 t/s n/a
Granite 4.1 30B 28.87B Just fits n/a 24.42 GB 3.4 t/s n/a
Mistral Small 24B Instruct 2501 23.57B Just fits n/a 24.98 GB 3.4 t/s n/a
Qwen3 32B 32.76B Just fits n/a 27.43 GB 3.1 t/s n/a
Qwen2.5 32B Instruct 32.76B Just fits n/a 27.43 GB 3.1 t/s n/a
Qwen2.5 Coder 32B Instruct 32.76B Just fits n/a 27.43 GB 3.1 t/s n/a
Llama 3.3 70B Instruct 70.55B Fits only at 2 to 3 bits n/a 28.14 GB 3 t/s n/a
Gemma 4 31B Instruct 31.27B Just fits n/a 27.58 GB 3 t/s n/a
Qwen3.5 27B 27.78B Just fits n/a 28.54 GB 3 t/s n/a
Qwen2.5 VL 72B Instruct 73.41B Fits only at 2 to 3 bits n/a 29.16 GB 2.9 t/s n/a
Qwen2.5 72B Instruct 72.71B Fits only at 2 to 3 bits n/a 28.91 GB 2.9 t/s n/a
Gemma 3 27B Instruct 27.43B Just fits n/a 28.65 GB 2.9 t/s n/a
Llama 3 3 Nemotron Super 49B V1 49.87B CPU only n/a 48.55 GB n/a n/a
Kimi K2.6 1026.88B Will not run n/a 578.41 GB n/a n/a
Kimi K2 Instruct 1026.41B Will not run n/a 578.15 GB n/a n/a
GLM 5.1 753.86B Will not run n/a 425.01 GB n/a n/a
GLM 5.2 753.33B Will not run n/a 424.72 GB n/a n/a
DeepSeek V3.2 685.4B Will not run n/a 386.4 GB n/a n/a
DeepSeek R1 684.53B Will not run n/a 385.92 GB n/a n/a
MiniMax M3 427.04B Will not run n/a 241.5 GB n/a n/a
GLM 4.5 358.34B Will not run n/a 204.77 GB n/a n/a
Qwen3 235B A22B 235.09B Will not run n/a 134.03 GB n/a n/a
MiniMax M2.5 228.7B Will not run n/a 130.88 GB n/a n/a
MiniMax M2.7 228.69B Will not run n/a 130.87 GB n/a n/a
Qwen3.5 122B A10B 125.09B Will not run n/a 71.01 GB n/a n/a
GPT OSS 120B 116.83B Will not run n/a 59.65 GB n/a n/a

What it will not run

35 of the 86 architectures we track are out of reach here, even at 2-bit precision.

If you want more headroom

The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.

DeviceMemoryBandwidthModels that fit
RTX 6000 Ada Generation 48 GB 960 GB/s 72 Check price
Radeon PRO W7900 48 GB 864 GB/s 72 Check price
RTX A6000 48 GB 768 GB/s 72 Check price
Ryzen AI Max+ 395 64GB 64 GB 256 GB/s 66 Check price

Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.

Other devices with 32 GB

Same capacity, different speed. Once a model fits, bandwidth is what separates these.

GeForce RTX 5090

1792 GB/s · NVIDIA

Apple M1 Max 32GB

400 GB/s · Apple

Apple M2 Max 32GB

400 GB/s · Apple

Ryzen AI Max+ 395 32GB

256 GB/s · AMD

Jetson AGX Orin 32GB

204.8 GB/s · NVIDIA

Apple M1 Pro 32GB

200 GB/s · Apple

Questions people ask about an Apple M4 32GB

How much VRAM does an Apple M4 32GB have?

The Apple M4 32GB has 32 GB of unified memory shared with the CPU, of which roughly 22.4 GB is available to a model once the operating system takes its share.

Can an Apple M4 32GB run a 70B model?

Yes, once the macOS GPU memory limit is raised. At IQ3_XXS it needs 28.95 GB and runs at around 34 tokens per second.

What is the best local model for an Apple M4 32GB?

GPT OSS 20B at MXFP4 is the strongest all-round fit: 11.81 GB used, around 19.8 tokens per second at 8k context.