Apple · M4 Pro · Laptop
Best AI Models for MacBook Pro 16" M4 Pro (24 GB)
Runs 14B models well and fits 35B MoE; dense 27–31B fits but is slow at 11 tok/s, and 70B doesn't fit in 20.5 GB.
| # | Model | Speed | llmrun Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 11.0 t/s | 11.0 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 11.0 t/s | 11.0 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 49.1 t/s | 49.1 tok/s | Q4_K_M · 16.5 GB | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 54.5 t/s | 54.5 tok/s | Q4_K_M · 13.1 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 62.7 t/s | 62.7 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed | Coding Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Q4_K_M · 16.5 GB · 49.1 t/s | 49.1 tok/s | Q4_K_M · 16.5 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 54.5 t/s | 54.5 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Q4_K_M · 7.9 GB · 36.1 t/s | 36.1 tok/s | Q4_K_M · 7.9 GB | |
| 4 | Qwen3.8 27BAlibaba Q4_K_M · 17.9 GB · 11.0 t/s | 11.0 tok/s | Q4_K_M · 17.9 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 62.7 t/s | 62.7 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed | Reasoning Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Q4_K_M · 16.5 GB · 49.1 t/s | 49.1 tok/s | Q4_K_M · 16.5 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 30.0 t/s | 30.0 tok/s | Q4_K_M · 6.6 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 54.5 t/s | 54.5 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 34.6 t/s | 34.6 tok/s | Q4_K_M · 7.6 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 62.7 t/s | 62.7 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed | Long context Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 30.0 t/s | 30.0 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 58.6 t/s | 58.6 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 54.5 t/s | 54.5 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 23.7 t/s | 23.7 tok/s | Q4_K_M · 11.0 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 62.7 t/s | 62.7 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 58.6 t/s | 58.6 tok/s | Q4_K_M · 3.4 GB | |
| 2 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 62.7 t/s | 62.7 tok/s | Q4_K_M · 15.0 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 54.5 t/s | 54.5 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 49.1 t/s | 49.1 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 30.0 t/s | 30.0 tok/s | Q4_K_M · 6.6 GB |
| # | Model | Speed | Vision Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Q4_K_M · 16.5 GB · 49.1 t/s | 49.1 tok/s | Q4_K_M · 16.5 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 30.0 t/s | 30.0 tok/s | Q4_K_M · 6.6 GB | |
| 3 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 58.6 t/s | 58.6 tok/s | Q4_K_M · 3.4 GB | |
| 4 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 23.8 t/s | 23.8 tok/s | Q4_K_M · 10.9 GB | |
| 5 | Q4_K_M · 6.5 GB · 30.8 t/s | 30.8 tok/s | Q4_K_M · 6.5 GB |
The MacBook Pro 16" M4 Pro is one of the fastest 24 GB machines: it runs 14B models well and fits 35B MoE. Among 24 GB machines, only the M5 Pro write faster. macOS keeps about 3.5 GB, leaving 20.5 GB for models. With 17 TFLOPS it reads long prompts faster than most 24 GB machines: a 4K-token prompt takes 66 s. At its $2,499 MSRP it gives 12.0 tok/s per $1,000, 6th of the 7 machines compared here. M4 Pro generation, released 2024; the newer M5 Pro writes 13% faster with 1.9× the compute.
MacBook Pro 16" M4 Pro strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 46.3 tok/s in 19.8 GB
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 20.5 GB usable here
- Slow on large dense models: Qwen3.8 27B at 11.0 tok/s
- Slow prompt reading: A 32K-token prompt on Qwen3.8 27B waits 8.8 min for the first token
- No CUDA: vLLM, AWQ, GPTQ and FP8 builds don't run; use GGUF or MLX
Memory for models
20.5 GB of 24 GB70B models need 51.3 GB at Q4, more than the 20.5 GB available. The 48 GB config fits Qwen3.8 27B →Or rent an H100 (80 GB) from $2.69/hr →
Speed per dollar and per watt
On Qwen3.5 9B (9B params) 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. The MacBook Pro 16" M4 Pro has no current new price, so it is shown at its MSRP.
| Machine | New price | tok/s | tok/s per $1,000 | tok/s per 100 W |
|---|---|---|---|---|
RTX 509032 GB | about $5,376 | 183 | 34.1 | 31.8 |
Mac Mini M4 Pro24 GB | about $1,599 | 30.0 | 18.8 | 21.4 |
Mac Studio M4 Max36 GB | about $2,499 | 45.1 | 18.0 | 31.1 |
| about $1,999 | 30.0 | 15.0 | – | |
| about $2,299 | 33.8 | 14.7 | – | |
| about $2,499 | 30.0 | 12.0 | – | |
MacBook Air 13" M424 GB | about $1,399 | 13.2 | 9.4 | – |
| Machine | Used price | tok/s | tok/s per $1,000 | tok/s per 100 W |
|---|---|---|---|---|
RTX 409024 GBBest used value | about $3,000 | 103 | 34.3 | 22.9 |
RTX 509032 GB | about $6,400 | 183 | 28.6 | 31.8 |
Mac Studio M4 Max36 GB | about $2,000 | 45.1 | 22.6 | 31.1 |
| about $2,109 | 33.8 | 16.0 | – |
Cost per token: the MacBook Pro 16" M4 Pro against 24–64 GB machines and GPUs
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Machine, MSRP
- $5.28
- Power
- not published
Renting costs as much as buying after ≈5,400 hours (≈3.7 years at 4 h a day).
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
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 Qwen3.5 9B.
Output speed depends on memory bandwidth (273 GB/s). Grey rows need more than the 20.5 GB available.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB30.0tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB20.1tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB11.0tok/s
- 46.3tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
Pro generations: writing vs compute
M1 Pro → M5 ProWriting speed follows memory bandwidth and barely moved: 22.0 to 33.8 tok/s since the M1 Pro. Compute, which sets how fast prompts are read, grew 3.1×. Indexed to the M1 Pro; speeds on Qwen3.5 9B, same 16 GB.
All 1125 models on the MacBook Pro 16" M4 Pro
Featured onlyBest quant that fits in 20.5 GB, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
VibeThinker 1.5BChatMathCode | Q4_K_M | 1.8 GB | 134.6 t/s | 131K | S95, Runs great | |
XCurOS0.1 8B InstructChatFunctions | BF16 | 16.5 GB | 12.2 t/s | 33K | B51, Decent | |
Granite 4.0 1BChatFunctions | Q4_K_M | 2.6 GB | 131.8 t/s | 131K | S95, Runs great | |
| Q4_K_M | 0.5 GB | 398.1 t/s | 33K | S100, Runs great | ||
Ouro 2.6B ThinkingChatReasoning | BF16 | 12.1 GB | 29.7 t/s | 66K | A71, Runs well | |
SmolVLM InstructVision | Q4_K_M | 4.9 GB | 93.2 t/s | 16K | S90, Runs great | |
Sarvam 30BChatFunctions | Q4_K_M | 19.9 GB | 54.8 t/s | 131K | B56, Decent | |
| Q4_K_M | 1.7 GB | 130.9 t/s | 4K | S95, Runs great | ||
| Q4_K_M | 5.3 GB | 38.0 t/s | 131K | A74, Runs well | ||
| Q4_K_M | 8.6 GB | 32.2 t/s | — | A71, Runs well | ||
| Q4_K_M | 4.2 GB | 65.9 t/s | 262K | S85, Runs great | ||
Hermes 3 Llama 3.1 8BChatRoleplayFunctions | Q4_K_M | 7.3 GB | 35.5 t/s | 131K | A73, Runs well | |
Falcon H1 7B InstructChatFunctions | Q4_K_M | 6.3 GB | 37.9 t/s | 262K | A74, Runs well | |
| Q4_K_M | 4.2 GB | 65.9 t/s | 262K | S85, Runs great | ||
Sweep Next Edit v2 7BChatCode | Q4_K_M | 5.8 GB | 38.3 t/s | 33K | A75, Runs well |
Specs
- Chip
- M4 Pro
- CPU cores
- 14
- GPU cores
- 20
- Unified memory
- 24 GB
- For models
- 20.5 GB (macOS keeps 3.5 GB)
- Memory bandwidth
- 273 GB/s
- FP16 (dense)
- 17.2 TFLOPS
- Neural Engine
- 38 TOPS
- Released
- 2024-11-08
- MSRP
- $2,499
MacBook Pro 16" M4 Pro FAQ
Is the MacBook Pro 16" M4 Pro (24 GB) worth it for local LLMs in 2026?
At its $2,499 MSRP it gives 12.0 tok/s per $1,000 on Qwen3.5 9B, 6th of the 7 machines compared here. It runs 14B models well and fits 35B MoE; dense 27–31B fits but is slow at 11 tok/s, and 70B doesn't fit in 20.5 GB.
How much memory is available for AI on the MacBook Pro 16" M4 Pro (24 GB)?
About 20.5 GB of the 24 GB. macOS keeps roughly 3.5 GB for itself and other apps.
What is the largest model the MacBook Pro 16" M4 Pro (24 GB) can run?
Qwen3.6 35B A3B (36.0B) is the largest at 4-bit or better: at IQ4_XS it uses 19.8 GB, 97% of the available memory, and runs at about 46.3 tok/s.
Can the MacBook Pro 16" M4 Pro (24 GB) run Qwen3.8 27B?
Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of the 20.5 GB available, and it is the best model that runs well on this machine.
Can the MacBook Pro 16" M4 Pro (24 GB) run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 20.5 GB available.
How long does the MacBook Pro 16" M4 Pro (24 GB) take to read a long prompt?
On Qwen3.8 27B, about 66 s for a 4K-token prompt and 8.8 min for a 32K-token document before the first word appears.
Is renting a GPU cheaper than buying the MacBook Pro 16" M4 Pro?
An RTX 5090 (32 GB) rents from $0.46/hr. Renting costs as much as buying after ≈5,400 hours (≈3.7 years at 4 h a day).