Qwen 3 14B needs ~15.3 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 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
Fit status
Runs well
Decode
9.6 tok/s
TTFT
20135 ms
Safe context
66K
Memory
15.3 GB / 23.0 GB
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 | S | Runs well | 9.6 tok/s | 10983 ms | 66K |
| Coding | S | Runs well | 9.6 tok/s | 20135 ms | 66K |
| Agentic Coding | S | Runs well | 9.6 tok/s | 29287 ms | 66K |
| Reasoning | S | Runs well | 9.6 tok/s | 23795 ms | 66K |
| RAG | S | Runs well | 9.6 tok/s | 36608 ms | 66K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen 3 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 151.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 96.9 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 88.4 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 87.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 82.9 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 34.2 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 18.4 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 6.8 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen 3 14B (14B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | S87 |
Q3_K_S | 3 | 6.9 GB | Low | S88 |
NVFP4 | 4 | 7.8 GB | Medium | S88 |
Q4_K_M | 4 | 8.5 GB | Medium | S89 |
Q5_K_M | 5 | 10.1 GB | High | S90 |
Q6_K | 6 | 11.5 GB | High | S91 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | S91 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Qwen 3 14B on your machine.
Run
ollama run qwen3Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 11.7 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.6 tok/s | |
| Qwen 3.6 27B | 27B | S | 7.1 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 12.4 tok/s | |
| Qwen 3.5 35B A3B | 35B | A | 10.2 tok/s |
Yes, MacBook Pro M4 32GB can run Qwen 3 14B with a S grade (Runs well). Expected decode speed: 9.6 tok/s.
Qwen 3 14B (14B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, Qwen 3 14B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20135ms using Q4_K_M quantization.
For coding workloads, Qwen 3 14B on MacBook Pro M4 32GB receives a S grade with 9.6 tok/s and 66K context.
On MacBook Pro M4 32GB, Qwen 3 14B can safely use up to 66K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 32GB 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-3-14b-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: