Qwen3-Coder-Next needs ~52.8 GB but MacBook Pro M4 16GB only has 11.5 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
41.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.8 tok/s
TTFT
51564 ms
Safe context
4K
Memory
52.8 GB / 11.5 GB
Offload
80%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 52.8 GB, but this setup only exposes 11.5 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 | 3.8 tok/s | 28126 ms | 4K |
| Coding | F | Too heavy | 3.8 tok/s | 51564 ms | 4K |
| Agentic Coding | F | Too heavy | 3.8 tok/s | 75002 ms | 4K |
| Reasoning | F | Too heavy | 3.8 tok/s | 60939 ms | 4K |
| RAG | F | Too heavy | 3.8 tok/s | 93753 ms | 4K |
How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 31.2 GB | Low | F0 |
Q3_K_S | 3 | 39.2 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.
~$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.
~$3,199 MSRP
No, Qwen3-Coder-Next requires more memory than MacBook Pro M4 16GB provides.
Qwen3-Coder-Next (80B parameters) requires approximately 52.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 16GB, Qwen3-Coder-Next achieves approximately 3.8 tokens per second decode speed with a time-to-first-token of 51564ms using Q4_K_M quantization.
For coding workloads, Qwen3-Coder-Next on MacBook Pro M4 16GB receives a F grade with 3.8 tok/s and 4K context.
On MacBook Pro M4 16GB, Qwen3-Coder-Next can safely use up to 4K tokens of context. The model's official context limit is 256K, 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-coder-next-on-m4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 48.8 GB | Medium | F0 |
Q5_K_M | 5 | 57.6 GB | High | F0 |
Q6_K | 6 | 65.6 GB | High | F0 |
Q8_0 | 8 | 85.6 GB | Very High | F0 |
F16 | 16 | 164.0 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 M4 16GB 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.
