Apple · M5 Pro · Laptop

Best AI Models for MacBook Pro 14-inch (M5 Pro) (64 GB)

Runs 14B and 35B MoE models well and fits 120B MoE; 27–31B and 70B fit but are slow (12 and 5 tok/s).

64 GB unified60.5 GB for models307 GB/s32 TFLOPS FP162026

Same chip in

MacBook Pro 14-inch$2,299 Mac MiniMacBook Pro 16"MacBook Pro 14"

What LLMs can the MacBook Pro 14-inch (M5 Pro) run?

#	Model	Speed	Quant · Memory
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 46.5 t/s	46.5 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 53.2 t/s	53.2 tok/s	Q4_K_M · 16.5 GB
3	Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 52.2 t/s	52.2 tok/s	Q4_K_M · 19.4 GB
4	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 59.3 t/s	59.3 tok/s	Q4_K_M · 13.1 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 58.4 t/s	58.4 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · Memory
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 46.5 t/s	46.5 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 53.2 t/s	53.2 tok/s	Q4_K_M · 16.5 GB
3	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 59.3 t/s	59.3 tok/s	Q4_K_M · 13.1 GB
4	Llama 3.1 8B InstructMeta Q4_K_M · 7.9 GB · 40.5 t/s	40.5 tok/s	Q4_K_M · 7.9 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 58.4 t/s	58.4 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · Memory
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 46.5 t/s	46.5 tok/s	Q4_K_M · 22.0 GB
2	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 53.2 t/s	53.2 tok/s	Q4_K_M · 16.5 GB
3	Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 33.8 t/s	33.8 tok/s	Q4_K_M · 6.6 GB
4	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 59.3 t/s	59.3 tok/s	Q4_K_M · 13.1 GB
5	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 58.4 t/s	58.4 tok/s	Q4_K_M · 19.3 GB

#	Model	Speed	Quant · Memory
1	Qwen3.6 35B A3BAlibabaHow to run → Q4_K_M · 22.0 GB · 46.5 t/s	46.5 tok/s	Q4_K_M · 22.0 GB
2	Qwen3.8 27BAlibaba Q4_K_M · 17.9 GB · 12.3 t/s	12.3 tok/s	Q4_K_M · 17.9 GB
3	Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 12.3 t/s	12.3 tok/s	Q4_K_M · 17.9 GB
4	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 58.4 t/s	58.4 tok/s	Q4_K_M · 19.3 GB
5	GLM 4.7 FlashZ.ai Q4_K_M · 19.9 GB · 43.5 t/s	43.5 tok/s	Q4_K_M · 19.9 GB

#	Model	Speed ↓	Quant · Memory
1	Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 65.9 t/s	65.9 tok/s	Q4_K_M · 3.4 GB
2	GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 59.3 t/s	59.3 tok/s	Q4_K_M · 13.1 GB
3	NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA Q4_K_M · 19.3 GB · 58.4 t/s	58.4 tok/s	Q4_K_M · 19.3 GB
4	Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 53.2 t/s	53.2 tok/s	Q4_K_M · 16.5 GB
5	Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 52.2 t/s	52.2 tok/s	Q4_K_M · 19.4 GB

See all 370 models on the MacBook Pro 14-inch (M5 Pro) →Bars = traction for models without a Score yet

About the MacBook Pro 14-inch (M5 Pro)

The MacBook Pro 14-inch (M5 Pro) is a 64 GB laptop with more memory than speed: it runs 14B and 35B MoE models well and fits 120B MoE. macOS keeps about 3.5 GB, leaving 60.5 GB for models. Prompt reading is its weak spot: a 4K-token prompt waits 6.0 s before the first word on Qwen3.6 35B A3B. At ≈$2,299 it gives 20.2 tok/s per $1,000, 4th of the 7 machines compared here. M5 Pro generation, released 2026.

Speed in 64 GB#8307 GB/s

Prompt reading#532 TFLOPS

For models60.5 GBmacOS keeps 3.5 GB

ChipM5 Proreleased 2026

MacBook Pro 14-inch (M5 Pro) strengths and weaknesses

Strengths

Runs 120B MoE models: GPT OSS 120B at 48.4 tok/s in 59.1 GB

Weaknesses

Slow on large dense models: Llama 3.3 70B Instruct at 4.6 tok/s, Qwen3.8 27B at 12.3 tok/s
Slow prompt reading: A 32K-token prompt on Qwen3.6 35B A3B waits 48.0 s for the first token
No CUDA: vLLM, AWQ, GPTQ and FP8 builds don't run; use GGUF or MLX

Memory for models

60.5 GB of 64 GB

9B6.6 GB14B11.9 GB35B MoE22 GB70B51.3 GB120B MoE59.1 GB

35B MoE22 GB120B MoE59.1 GB

For models 60.5 GBmacOS keeps 3.5 GBModel size at Q4

Everything on the size ladder fits. Qwen3.6 35B A3B and Gemma 4 26B A4B IT together use 38.5 GB and leave 22 GB free.

Speed per dollar and per watt

NewUsed

On Qwen3.6 35B A3B (35B params, 3B active) at Q4_K_M. New means a shop price, either a street quote or the maker's own price; used is compared with used. The last two columns are the speed you get per $1,000 of price and per 100 W of power; higher is better.

Machine	New price	tok/s	tok/s per $1,000	tok/s per 100 W
RTX 509032 GB	about $5,376	213	39.7	37.1
Mac Studio M4 Max64 GB	about $2,699	64.2	23.8	44.3
Framework Desktop Ryzen AI Max+ 395128 GB	about $1,999	46.7	23.4	33.4
MacBook Pro 14 M5 Pro64 GB	about $2,299	46.5	20.2	–
Mac Mini M4 Pro48 GB	about $2,199	43.2	19.6	30.9
Mac Studio M3 Ultra96 GB	about $3,999	76.6	19.2	28.4
MacBook Pro 16" M4 Max64 GB	about $4,199	64.2	15.3	–

Machine	Used price	tok/s	tok/s per $1,000	tok/s per 100 W
RTX 409024 GBBest used value	about $3,000	151	50.3	33.6
RTX 509032 GB	about $6,400	213	33.3	37.1
MacBook Pro 14 M5 Pro64 GB	about $2,109	46.5	22.0	–
Mac Studio M4 Max64 GB	about $3,400	64.2	18.9	44.3
Mac Studio M3 Ultra96 GB	about $4,525	76.6	16.9	28.4

Cost per token: the MacBook Pro 14-inch (M5 Pro) against 24–128 GB machines and GPUs

On Qwen3.6 35B A3B (35B params, 3B active, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.

NewUsed

Speed$ per 1M tokens

24–36 GB48–64 GB96–128 GBthis laptopat MSRP

Cost per 1M output tokenson Qwen3.6 35B A3B · card over 3 years + power

$3.14

Machine, new price: $3.14
Power: not published

3-year cost of ownership$2,299

Or rent an A100 in the cloud$1.00/hr

Renting costs as much as buying after ≈2,300 hours (≈1.6 years at 4 h a day).

Power$/kWhUseh/dayKeepyears

Each dot is a machine or GPU; hover or tap it for its numbers, click to open it. Machines without a price for the chosen basis are left out. Machines without a published power figure are left out of Per watt and costed without power. How we calculate cost per token

Similar devices and alternatives

Other memory sizes of the same machine first, then machines and GPUs people weigh against it, each with the one reason it is worth a look. Speeds on Qwen3.6 35B A3B.

38% faster, same memoryMac Studio M4 Max (64 GB)Apple · 60.5 GB for models · M4 Max

▲ 38% speedsame memory$2,699

Machine, 7% slowerMac Mini M4 Pro (48 GB)Apple · 44.5 GB for models · M4 Pro

▼ 7% speed▼ 16 GB$2,199

65% faster, 32 GB more memoryMac Studio M3 Ultra (96 GB)Apple · 92.5 GB for models · M3 Ultra

▲ 65% speed▲ 32 GB$3,999

35.5 GB more memory for bigger modelsFramework Desktop Ryzen AI Max+ 395Framework · 96 GB for models · Ryzen AI Max+ 395

same speed▲ 35.5 GB$1,999

3.2× faster if your models fit in 24 GBRTX 4090NVIDIA · 24 GB VRAM · GPU

▲ 225% speed▼ 36.5 GB$1,599rent $0.34/hr

4.6× faster if your models fit in 32 GBRTX 5090NVIDIA · 32 GB VRAM · GPU

▲ 359% speed▲ $3,077 more▼ 28.5 GB$5,376rent $0.48/hr

MacBook Pro 14-inch (M5 Pro) tokens per second by model size

Output speed depends on memory bandwidth (307 GB/s). Grey rows need more than the 60.5 GB available.

9BQwen3.5 9B · Q4_K_M · 6.6 GB
33.8tok/s
14BQwen3 14B · Q4_K_M · 11.9 GB
22.6tok/s
27–31BQwen3.8 27B · Q4_K_M · 17.9 GB
12.3tok/s
35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB
46.5tok/s
70BLlama 3.3 70B Instruct · Q4_K_M · 51.3 GB slow
4.6tok/s
120B MoEGPT OSS 120B · Q4_0 · 59.1 GB tight · Q4_0
48.4tok/s

Estimated speeds: Q4_K_M, 4K context, one user. How we estimate

Pro generations: writing vs compute

M1 Pro → M5 Pro

Writing speed (tok/s)Compute (TFLOPS FP16)

Writing speed follows memory bandwidth and barely moved: 34.8 to 46.5 tok/s since the M1 Pro. Compute, which sets how fast prompts are read, grew 3.1×. Indexed to the M1 Pro; speeds on Qwen3.6 35B A3B, same 32 GB.

All 1229 models on the MacBook Pro 14-inch (M5 Pro)

Featured only

Best quant that fits in 60.5 GB, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works

LLM models compatible with MacBook Pro 14-inch (M5 Pro) — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Llama Poro 2 8B Instruct8.0BChat Q4_K_M·39.9 t/s	Q4_K_M	6.2 GB10%	39.9 t/s	8K	A75, Runs well
ERNIE 4.5 VL 28B A3B PT29.4BVision BF16·29.7 t/s	BF16	60.0 GB99%	29.7 t/s	131K	C41, Usable
Qwen3Guard Gen 0.6B752MChat Q4_K_M·247.0 t/s	Q4_K_M	1.7 GB3%	247.0 t/s	33K	S100, Runs great
Tiny Mixtral247MChat Q4_K_M·727.9 t/s	Q4_K_M	0.5 GB1%	727.9 t/s	131K	S100, Runs great
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic v227.4BChatReasoning Q4_K_M·12.5 t/s	Q4_K_M	17.6 GB29%	12.5 t/s	262K	B51, Decent
Jan v3 4B Base Instruct4.4BChatCode Q4_K_M·68.4 t/s	Q4_K_M	4.5 GB7%	68.4 t/s	262K	S85, Runs great
Supra 50M Base52MChat Q4_K_M·596.9 t/s	Q4_K_M	0.3 GB1%	596.9 t/s	1K	S100, Runs great
Granite 4.0 H Tiny6.9BChatFunctions Q4_K_M·79.7 t/s	Q4_K_M	5.8 GB10%	79.7 t/s	131K	S88, Runs great
T Pro IT 2.132.8BChat Q4_K_M·10.6 t/s	Q4_K_M	22.6 GB37%	10.6 t/s	41K	B49, Decent
Qwen3 VL 2B Thinking2.1BVisionFunctions Q4_K_M·118.7 t/s	Q4_K_M	3.5 GB6%	118.7 t/s	262K	S95, Runs great
LFM2.5 1.2B Instruct1.2BChatFunctions Q4_K_M·200.8 t/s	Q4_K_M	1.2 GB2%	200.8 t/s	128K	S100, Runs great
Salamandra 2B Instruct2.3BChat Q4_K_M·104.8 t/s	Q4_K_M	3.3 GB5%	104.8 t/s	8K	S92, Runs great
Gemma 2 9B IT9.2BChat Q4_K_M·35.2 t/s	Q4_K_M	7.3 GB12%	35.2 t/s	8K	A73, Runs well
Solar Open 100B102.7BChatFunctions Q4_K_S·24.1 t/s	Q4_K_S	59.6 GB99%	24.1 t/s	131K	C35, Usable
Param 1 2.9B Instruct2.9BChat BF16·34.2 t/s	BF16	7.1 GB12%	34.2 t/s	8K	A73, Runs well

MacBook Pro 14-inch (M5 Pro) specs

Specs

Chip: M5 Pro
CPU cores: 18
GPU cores: 20
Unified memory: 64 GB LPDDR5X
For models: 60.5 GB (macOS keeps 3.5 GB)
Memory bandwidth: 307 GB/s
FP16 (dense): 32.3 TFLOPS
Neural Engine: 38 TOPS
NPU: 16-core Neural Engine
Size: MacBook Pro 14/16-inch
Released: 2026-03-11
MSRP: $2,199

MacBook Pro 14-inch (M5 Pro) FAQ

Is the MacBook Pro 14-inch (M5 Pro) worth it for local LLMs in 2026?

At ≈$2,299 it gives 20.2 tok/s per $1,000 on Qwen3.6 35B A3B, 4th of the 7 machines compared here. It runs 14B and 35B MoE models well and fits 120B MoE; 27–31B and 70B fit but are slow (12 and 5 tok/s).

How much memory is available for AI on the MacBook Pro 14-inch (M5 Pro)?

About 60.5 GB of the 64 GB. macOS keeps roughly 3.5 GB for itself and other apps.

What is the largest model the MacBook Pro 14-inch (M5 Pro) can run?

GPT OSS 120B (116.8B) is the largest at 4-bit or better: at Q4_0 it uses 59.1 GB, 98% of the available memory, and runs at about 48.4 tok/s. At 3-bit, a bigger model fits too: NVIDIA Nemotron 3 Super 120B A12B BF16 (123.6B) at IQ3_M, 56.1 GB.

Can the MacBook Pro 14-inch (M5 Pro) run Qwen3.6 35B A3B?

Yes. Qwen3.6 35B A3B fits at Q4_K_M, using 22.0 GB of the 60.5 GB available, and it is the best model that runs well on this machine.

Can the MacBook Pro 14-inch (M5 Pro) run 70B models?

Yes, but slowly: Llama 3.3 70B Instruct at Q4_K_M uses 51.3 GB and writes about 4.6 tok/s.

How long does the MacBook Pro 14-inch (M5 Pro) take to read a long prompt?

On Qwen3.6 35B A3B, about 6.0 s for a 4K-token prompt and 48 s for a 32K-token document before the first word appears.

Is renting a GPU cheaper than buying the MacBook Pro 14-inch (M5 Pro)?

An A100 (80 GB) rents from $1.00/hr. Renting costs as much as buying after ≈2,300 hours (≈1.6 years at 4 h a day).

48 GB GPUs All Macs Laptops Mac Pro M2 Ultra Mac Studio M1 Ultra Mac Studio M2 Ultra Mac Studio M5 Max MacBook Pro 14" M5 Max MacBook Pro 16" M5 Max Mac Studio M2 Max Best coding LLMs All hardware