Qwen 3.6 27B needs ~19.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With NVFP4 quantization, expect ~16 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
3.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.0 tok/s
TTFT
14884 ms
Safe context
4K
Memory
20.9 GB / 17.3 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~2.6 GB host RAM) | 13.4 tok/s | 7881 ms | 4K |
| Coding | F | Too heavy | 13.0 tok/s | 14884 ms | 4K |
| Agentic Coding | F | Too heavy | 12.3 tok/s | 22893 ms | 4K |
| Reasoning | F | Too heavy | 13.0 tok/s | 17590 ms | 4K |
| RAG | F | Too heavy | 12.3 tok/s | 28617 ms | 4K |
How Qwen 3.6 27B (27B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 10.5 GB | Low | S93 |
Q3_K_S | 3 | 13.2 GB | Low | F0 |
NVFP4 | 4 | 15.1 GB | Medium | F0 |
Q4_K_M | 4 | 16.5 GB | Medium | F0 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Qwen 3.6 27B on your machine.
Run
lms load Qwen3.6-27B && lms server startアップグレードオプション
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.
〜$799 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.
〜$1,099 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.
〜$1,099 MSRP
RTX 5090 32GB最大の飛躍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.
〜$1,999 MSRP
Yes, MacBook Pro M4 Pro 24GB can run Qwen 3.6 27B at NVFP4 quantization (Very compromised (needs ~1.8 GB host RAM)). The recommended Q4_K_M requires 20.9 GB which exceeds available memory, but at NVFP4 it needs only 19.6 GB. Expected decode speed: 16.2 tok/s.
Qwen 3.6 27B (27B parameters) requires approximately 20.9 GB at Q4_K_M quantization. On MacBook Pro M4 Pro 24GB, it fits at NVFP4 using 19.6 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M4 Pro 24GB the best fitting quantization is NVFP4, which uses 19.6 GB.
On MacBook Pro M4 Pro 24GB, Qwen 3.6 27B achieves approximately 16.2 tokens per second decode speed with a time-to-first-token of 11950ms using NVFP4 quantization.
For coding workloads, Qwen 3.6 27B on MacBook Pro M4 Pro 24GB receives a F grade with 13.0 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, Qwen 3.6 27B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M4 Pro 24GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3.6-27b-on-m4-pro-24gb" 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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