Qwen 3 14B needs ~13.5 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~61 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
Tight fit
Decode
60.9 tok/s
TTFT
3181 ms
Safe context
33K
Memory
13.5 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 60.9 tok/s | 1735 ms | 33K |
| Coding | S | Tight fit | 60.9 tok/s | 3181 ms | 33K |
| Agentic Coding | S | Runs with offload | 60.9 tok/s | 4627 ms | 33K |
| Reasoning | S | Tight fit | 60.9 tok/s | 3759 ms | 33K |
| RAG | S | Runs with offload | 60.9 tok/s | 5784 ms | 33K |
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 RTX 6000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | S90 |
Q3_K_S | 3 | 6.9 GB | Low | S91 |
NVFP4 | 4 | 7.8 GB | Medium | S92 |
Q4_K_M | 4 | 8.5 GB | Medium | S92 |
Q5_K_M | 5 | 10.1 GB | High | S92 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | S91 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Qwen 3 14B on your machine.
Run
ollama run qwen3Yes, RTX 6000 Ada Laptop 16GB can run Qwen 3 14B with a S grade (Tight fit). Expected decode speed: 60.9 tok/s.
Qwen 3 14B (14B parameters) requires approximately 13.5 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 RTX 6000 Ada Laptop 16GB, Qwen 3 14B achieves approximately 60.9 tokens per second decode speed with a time-to-first-token of 3181ms using Q4_K_M quantization.
For coding workloads, Qwen 3 14B on RTX 6000 Ada Laptop 16GB receives a S grade with 60.9 tok/s and 33K context.
On RTX 6000 Ada Laptop 16GB, Qwen 3 14B can safely use up to 33K tokens of context. The model's official context limit is 131K, 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-3-14b-on-rtx-6000-ada-laptop-16gb" 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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