Qwen 3.5 122B A10B needs ~81.2 GB but MacBook Pro M1 Pro 32GB only has 23.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
58.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.6 tok/s
TTFT
53109 ms
Safe context
4K
Memory
81.2 GB / 23.0 GB
Offload
70%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 81.2 GB, but this setup only exposes 23.0 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.6 tok/s | 28968 ms | 4K |
| Coding | F | Too heavy | 3.3 tok/s | 58087 ms | 4K |
| Agentic Coding | F | Too heavy | 3.6 tok/s | 77249 ms | 4K |
| Reasoning | F | Too heavy | 3.6 tok/s | 62765 ms | 4K |
| RAG | F | Too heavy | 3.6 tok/s | 96561 ms | 4K |
How Qwen 3.5 122B A10B (122B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 47.6 GB | Low | F0 |
Q3_K_S | 3 | 59.8 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
No, Qwen 3.5 122B A10B requires more memory than MacBook Pro M1 Pro 32GB provides.
Qwen 3.5 122B A10B (122B parameters) requires approximately 81.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 32GB, Qwen 3.5 122B A10B achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 58087ms using Q4_K_M quantization.
For coding workloads, Qwen 3.5 122B A10B on MacBook Pro M1 Pro 32GB receives a F grade with 3.3 tok/s and 4K context.
On MacBook Pro M1 Pro 32GB, Qwen 3.5 122B A10B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3.5-122b-a10b-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
68.3 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 74.4 GB | Medium | F0 |
Q5_K_M | 5 | 87.8 GB | High | F0 |
Q6_K | 6 | 100.0 GB | High | F0 |
Q8_0 | 8 | 130.5 GB | Very High | F0 |
F16 | 16 | 250.1 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M1 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
