Qwen 3.5 122B A10B needs ~79.7 GB but MacBook Pro M3 Pro 18GB only has 13.0 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
66.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.1 tok/s
TTFT
63051 ms
Safe context
4K
Memory
79.7 GB / 13.0 GB
Offload
80%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 79.7 GB, but this setup only exposes 13.0 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 | 3.1 tok/s | 34392 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 63051 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 91711 ms | 4K |
| Reasoning | F | Too heavy | 3.1 tok/s | 74515 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 114639 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen 3.5 122B A10B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~35 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 34.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 28.9 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 27.4 | Offloads |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 21.4 | Offloads |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 11.3 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 10.0 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 7.6 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 7.2 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 7.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 6.5 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 6.4 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 5.9 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 5.7 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 4.6 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 4.2 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 4.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 3.7 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.3 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen 3.5 122B A10B (122B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 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 | 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 |
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 M3 Pro 18GB provides.
Qwen 3.5 122B A10B (122B parameters) requires approximately 79.7 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 M3 Pro 18GB, Qwen 3.5 122B A10B achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 63051ms using Q4_K_M quantization.
For coding workloads, Qwen 3.5 122B A10B on MacBook Pro M3 Pro 18GB receives a F grade with 3.1 tok/s and 4K context.
On MacBook Pro M3 Pro 18GB, 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.
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 M3 Pro 18GB 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.
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<iframe src="https://willitrunai.com/embed/qwen-3.5-122b-a10b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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