Phi 3 Medium 14B needs ~13.6 GB but RTX 3070 Ti 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
5.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.5 tok/s
TTFT
14295 ms
Safe context
4K
Memory
13.6 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.6 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 | 17.4 tok/s | 6069 ms | 4K |
| Coding | F | Too heavy | 12.6 tok/s | 15368 ms | 4K |
| Agentic Coding | F | Too heavy | 8.8 tok/s | 31849 ms | 4K |
| Reasoning | F | Too heavy | 13.5 tok/s | 16895 ms | 4K |
| RAG | F | Too heavy | 8.8 tok/s | 39812 ms | 4K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | F0 |
Q3_K_S | 3 | 6.9 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
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 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.
~$499 MSRP
No, Phi 3 Medium 14B requires more memory than RTX 3070 Ti 8GB provides.
Phi 3 Medium 14B (14B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3070 Ti 8GB, Phi 3 Medium 14B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15368ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on RTX 3070 Ti 8GB receives a F grade with 12.6 tok/s and 4K context.
On RTX 3070 Ti 8GB, Phi 3 Medium 14B can safely use up to 4K tokens of context. The model's official context limit is 128K, 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-3-medium-14b-on-rtx-3070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 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.
