Qwen3.5 122B A10B needs ~88.8 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q3_K_M quantization, expect ~9 tok/s.
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
186.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
279.1 GB / 92.2 GB
Offload
70%
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 9.3 tok/s | 11302 ms | 20K |
| Coding | C | Runs with offload | 9.3 tok/s | 20721 ms | 20K |
| Agentic Coding | D | Very compromised (needs ~6.3 GB host RAM) | 7.8 tok/s | 36145 ms | 20K |
| Reasoning | C | Runs with offload | 9.3 tok/s | 24488 ms | 20K |
| RAG | D | Very compromised (needs ~6.3 GB host RAM) | 7.8 tok/s | 45181 ms |
How Qwen3.5 122B A10B (122B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 47.6 GB | Low | C48 |
Q3_K_S | 3 | 59.8 GB | Low | C48 |
NVFP4 | 4 |
Copy-paste commands to run Qwen3.5 122B A10B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-122B-A10B-GGUF" \
--hf-file "Qwen3.5-122B-A10B-GGUF-Q3_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 877%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBest valueRaises estimated decode speed by about 152%.
~$9,999 MSRP
Raises estimated decode speed by about 520%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Yes, MacBook Pro M4 Max 128GB can run Qwen3.5 122B A10B with a C grade (Runs with offload). Expected decode speed: 9.3 tok/s.
Qwen3.5 122B A10B (122B parameters) requires approximately 88.8 GB of memory with Q3_K_M quantization.
The recommended quantization for Qwen3.5 122B A10B is Q3_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 128GB, Qwen3.5 122B A10B achieves approximately 9.3 tokens per second decode speed with a time-to-first-token of 20721ms using Q3_K_M quantization.
For coding workloads, Qwen3.5 122B A10B on MacBook Pro M4 Max 128GB receives a C grade with 9.3 tok/s and 20K context.
On MacBook Pro M4 Max 128GB, Qwen3.5 122B A10B can safely use up to 20K tokens of context. The model's official context limit is —, 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/hf-unsloth--qwen3-5-122b-a10b-gguf-on-m4-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 20K |
68.3 GB |
| Medium |
| C48 |
Q4_K_MBest for your GPU | 4 | 74.4 GB | Medium | C48 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M4 Max 128GB 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.