Phi-4-reasoning-plus 14B needs ~14.0 GB but RTX 2000 Ada Laptop 8GB only has 8.0 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
6.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.2 tok/s
TTFT
37527 ms
Safe context
4K
Memory
14.0 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 14.0 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.6 tok/s | 16060 ms | 4K |
| Coding | F | Too heavy | 5.2 tok/s | 37527 ms | 4K |
| Agentic Coding | F | Too heavy | 3.4 tok/s | 82627 ms | 4K |
| Reasoning | F | Too heavy | 5.2 tok/s | 44350 ms | 4K |
| RAG | F | Too heavy | 3.4 tok/s | 103284 ms | 4K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | F0 |
Q3_K_S | 3 | 7.2 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 5060 Ti 16GBBudget pickMakes 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.
~$449 MSRP
RTX 4060 Ti 16GBBest valueMakes 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.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA upgradeMakes 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.
~$749 MSRP
No, Phi-4-reasoning-plus 14B requires more memory than RTX 2000 Ada Laptop 8GB provides.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2000 Ada Laptop 8GB, Phi-4-reasoning-plus 14B achieves approximately 5.2 tokens per second decode speed with a time-to-first-token of 37527ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on RTX 2000 Ada Laptop 8GB receives a F grade with 5.2 tok/s and 4K context.
On RTX 2000 Ada Laptop 8GB, Phi-4-reasoning-plus 14B can safely use up to 4K tokens of context. The model's official context limit is 33K, 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/phi-4-reasoning-plus-14b-on-rtx-2000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
8.2 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 9.0 GB | Medium | F0 |
Q5_K_M | 5 | 10.6 GB | High | F0 |
Q6_K | 6 | 12.1 GB | High | F0 |
Q8_0 | 8 | 15.7 GB | Very High | F0 |
F16 | 16 | 30.1 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
