MacBook Pro M5 Max 128GB Price & Used Prices: Which Local LLMs Can It Run?
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The MacBook Pro M5 Max 128GB (Apple, 2026) runs Qwen3.8 Flash Next at 4-bit at about 77 tokens/s. For long runs get the 16-inch; the 14-inch throttles.
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MacBook Pro M5 Max 128GB price history
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Full MacBook Pro M5 Max 128GB specifications
- Memory128 GB · LPDDR5X-9600 · unified memory
- Bandwidth614 GB/s
- Chip
- Apple M5 Max
- CPU
- 18-core
- GPU
- 40-core
- Storage
- 2 TB / 4 TB / 8 TB
- Ecosystem
- Metal
- Power
- 140 W
- Weight
- 1.62 kg (14″) / 2.15 kg (16″)
- Model identifier
- Mac17,7 (14″) / Mac17,6 (16″)
- Type
- Unified memory
- Launch date
- Mar 4, 2026
Best MacBook Pro M5 Max 128GB deals on eBay
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As an eBay Partner, VRAMGlass may be compensated if you buy through the eBay links on this page. The price you pay stays the same.
Used2026 Apple MacBook Pro 16" M5 Max 18-Core 128GB 2TB Black Z1MZ0002A$5,900+ shippingBuy on eBay
UsedApple MacBook Pro 16" M5 Max 128GB 2TB SSD 18/40- Warranty to May 2027 - Mint$6,189+$28 shippingBuy on eBay
UsedMacBook Pro 16 2026 M5 Max 18-Core CPU 40-Core GPU 128GB 4TB SSD ExcellentTop Rated$6,800Buy on eBay
New2026 Apple MacBook Pro 16" M5 Max 18-Core 128GB 2TB Black Z1MZ0002A NEW SEALEDTop Rated$6,900Buy on eBay
UsedMacBook Pro 14 2026 M5 Max 18-Core CPU 40-Core GPU 128GB 4TB SSD SilverTop Rated$7,000Buy on eBay
Used2026 Apple MacBook Pro 16" M5 Max 18-Core 128GB 4TB Black Z1MZ0002N 4 CYCLE READ$7,299+$44 shippingBuy on eBay
What LLMs can a single MacBook Pro M5 Max 128GB run?
This MacBook Pro tops out at the 176B Qwen3.8 Flash Next at 4-bit, and at 2-bit fits the 320B GLM 5.3 Flash. Raise the macOS GPU memory limit first.
| Model | Size | Runs? | Speed t/s | Max context |
|---|---|---|---|---|
 Gemma 4 31B30.7B | 11.8 GB | Runs | 35.7 | 256K |
 Qwen3.6 35B A3B36B (3B active) | 12.3 GB | Runs | 141.7 | 256K |
| Qwen3.8 Flash Next176B (6B active) | 78.9 GB | Runs | 94.1 | 256K |
 DeepSeek V4 Flash284B (13B active) | 96.8 GB | Runs | 70.6 | 1M |
 Hy3295B (21B active) | 107.2 GB | Runs | 42.2 | 86K |
 GLM 5.3 Flash320B (18B active) | 108.7 GB | Runs | 56.2 | 1M |
| Model | Size | Runs? | Speed t/s | Max context |
|---|---|---|---|---|
| Gemma 4 31B30.7B | 18.3 GB | Runs | 23.9 | 256K |
| Qwen3.6 35B A3B36B (3B active) | 22.1 GB | Runs | 113.7 | 256K |
| Qwen3.8 Flash Next176B (6B active) | 111.3 GB | Runs | 77.1 | 256K |
| DeepSeek V4 Flash284B (13B active) | 155.1 GB | Too big | — | — |
| Hy3295B (21B active) | 182.2 GB | Too big | — | — |
| GLM 5.3 Flash320B (18B active) | 199.7 GB | Too big | — | — |
| Model | Size | Runs? | Speed t/s | Max context |
|---|---|---|---|---|
| Gemma 4 31B30.7B | 32.6 GB | Runs | 13.8 | 256K |
| Qwen3.6 35B A3B36B (3B active) | 36.9 GB | Runs | 87.6 | 256K |
| Qwen3.8 Flash Next176B (6B active) | 188.2 GB | Too big | — | — |
| DeepSeek V4 Flash284B (13B active) | 161.9 GB | Too big | — | — |
| Hy3295B (21B active) | 317.7 GB | Too big | — | — |
| GLM 5.3 Flash320B (18B active) | 341 GB | Too big | — | — |
RunsFits fully in VRAM; speed is estimated from VRAM bandwidth.
Too bigWon’t fit on one card at this quantization, even with RAM offload.
Full calculation rules are on the Methodology page.
Who it suits
Good fit
- You often feed it long documents or whole codebases
The neural accelerators specifically speed up prompt processing
- You care about answer quality
Qwen3.6 35B A3B at 8-bit (36.9 GB) still runs at about 88 tokens/s
- You want to take 100B-class or larger models on the road
Qwen3.8 Flash Next at 4-bit runs at about 77 tokens/s and GLM 5.3 Flash at 2-bit at about 56, both after you raise the GPU memory limit
Not a good fit
- You pick the 14-inch but need long stretches at full load
In Notebookcheck's stress test, GPU performance dropped by up to 25%
- You want speed on dense models of 27B and up
Qwen3.8 27B at 4-bit runs at about 27 tokens/s, less than 40% of an RTX 5090's speed
Before you buy
14-inch or 16-inch
If you'll mainly run models for long periods, choose the 16-inch. In Notebookcheck's testing, the 14-inch's GPU slowed noticeably under sustained load while the 16-inch stayed largely steady, and it also scored about 12% higher in 3DMark Steel Nomad. Both sizes need the 40-core GPU version to get 128GB. Batteries and adapters are the same as the previous generation: 72.4 Wh with a bundled 96W adapter on the 14-inch, and 100 Wh with 140W on the 16-inch.
The 16-inch drains its battery even while plugged in
Notebookcheck measured the 16-inch drawing up to about 150W with the CPU and GPU both at full load, more than its bundled 140W adapter, so it still loses about 12% of its battery per hour while plugged in. A 180W adapter doesn't help, because the machine won't draw any more power from it.
Make sure your software uses the neural accelerators
The M5's neural accelerators need macOS 26.2 or later plus software that supports them: Apple's MLX, Ollama's MLX engine and recent official llama.cpp all do, while LM Studio's bundled llama.cpp runtime still has an unresolved bug report. What they speed up is prompt processing, not generation: in Apple's tests of the base M5 against the M4, time to first token was 3.3 to 4 times faster, but generation was only about 20% faster, because generation depends on memory bandwidth.
Compared with similar machines
MacBook Pro M5 Max 128GB vs MacBook Pro M4 Max 128GB and Mac Studio M5 Max 128GB: price, memory, bandwidth, compute and local LLM speed compared.
| Spec | MacBook Pro M5 Max 128GB | MacBook Pro M4 Max 128GB | Mac Studio M5 Max 128GB |
|---|---|---|---|
| Memory | 128 GB | 128 GBSame | 128 GBSame |
| Bandwidth | 614 GB/s | 546 GB/s11% lower | 614 GB/sSame |
| BF16 / FP16 compute | — | — | — |
| INT8 compute | — | — | — |
| Qwen3.8 27B speed (2-bit) | 43.9 t/s | 39.3 t/s10% slower | 43.9 t/sSame |
| Largest dense model (4-bit) | 222B | 222BSame | 222BSame |
| Power | 140 W | 140 WSame | 480 W243% higher |
| Released | 2026-03 | 2024-10 | 2026-08 |
| eBay median |
As an eBay Partner, VRAMGlass may be compensated if you buy through the eBay links on this page. The price you pay stays the same.
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