Qwen3-Coder-Next needs ~53.7 GB but MacBook Pro M4 Pro 24GB only has 17.3 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
36.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.1 tok/s
TTFT
21194 ms
Safe context
4K
Memory
53.7 GB / 17.3 GB
Offload
70%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 53.7 GB, but this setup only exposes 17.3 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 | 9.1 tok/s | 11560 ms | 4K |
| Coding | F | Too heavy | 9.1 tok/s | 21194 ms | 4K |
| Agentic Coding | F | Too heavy | 9.1 tok/s | 30827 ms | 4K |
| Reasoning | F | Too heavy | 9.1 tok/s | 25047 ms | 4K |
| RAG | F | Too heavy | 9.1 tok/s | 38534 ms | 4K |
How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 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 | 44.8 GB | 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 |
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.
〜$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 Pro 24GB provides.
Qwen3-Coder-Next (80B parameters) requires approximately 53.7 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 Pro 24GB, Qwen3-Coder-Next achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21194ms using Q4_K_M quantization.
For coding workloads, Qwen3-Coder-Next on MacBook Pro M4 Pro 24GB receives a F grade with 9.1 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, 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.
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 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-coder-next-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>
Preview:
