Qwen3-Coder-Next needs ~40.4 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q2_K quantization, expect ~27 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
11.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.6 tok/s
TTFT
13277 ms
Safe context
4K
Memory
58.0 GB / 46.1 GB
Offload
20%
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 14.8 tok/s | 7131 ms | 4K |
| Coding | F | Too heavy | 14.6 tok/s | 13277 ms | 4K |
| Agentic Coding | F | Too heavy | 14.1 tok/s | 19902 ms | 4K |
| Reasoning | F | Too heavy | 14.6 tok/s | 15691 ms | 4K |
| RAG | F | Too heavy | 14.1 tok/s | 24878 ms | 4K |
How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 31.2 GB | Low | S88 |
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 |
Copy-paste commands to run Qwen3-Coder-Next on your machine.
Run
ollama run qwen3-coder-nextUpgrade-Optionen
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.
ca. $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.
ca. $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.
ca. $3,199 MSRP
Yes, MacBook Pro M4 Pro 64GB can run Qwen3-Coder-Next at Q2_K quantization (Tight fit). The recommended Q4_K_M requires 58.0 GB which exceeds available memory, but at Q2_K it needs only 40.4 GB. Expected decode speed: 27.0 tok/s.
Qwen3-Coder-Next (80B parameters) requires approximately 58.0 GB at Q4_K_M quantization. On MacBook Pro M4 Pro 64GB, it fits at Q2_K using 40.4 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M4 Pro 64GB the best fitting quantization is Q2_K, which uses 40.4 GB.
On MacBook Pro M4 Pro 64GB, Qwen3-Coder-Next achieves approximately 27.0 tokens per second decode speed with a time-to-first-token of 7174ms using Q2_K quantization.
For coding workloads, Qwen3-Coder-Next on MacBook Pro M4 Pro 64GB receives a F grade with 14.6 tok/s and 4K context.
On MacBook Pro M4 Pro 64GB, Qwen3-Coder-Next can safely use up to 78K tokens of context at Q2_K quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 Pro 64GB 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-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: