Phi 4 reasoning vision 15B needs ~12.9 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
4.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.1 tok/s
TTFT
14770 ms
Safe context
4K
Memory
12.9 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 12.9 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 | 15.2 tok/s | 6945 ms | 4K |
| Coding | F | Too heavy | 13.1 tok/s | 14770 ms | 4K |
| Agentic Coding | F | Too heavy | 10.0 tok/s | 28107 ms | 4K |
| Reasoning | F | Too heavy | 13.1 tok/s | 17455 ms | 4K |
| RAG | F | Too heavy | 10.0 tok/s | 35134 ms | 4K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | F0 |
Q3_K_S | 3 | 7.4 GB | Low | F0 |
NVFP4 | 4 | 8.4 GB | Medium | F0 |
Q4_K_M | 4 | 9.2 GB | Medium | F0 |
Q5_K_M | 5 | 10.8 GB | High | F0 |
Q6_K | 6 | 12.3 GB | High | F0 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Upgrade-Optionen
RTX 3060 12GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
ca. $329 MSRP
RTX 5060 Ti 16GBGünstige WahlMakes 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. $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.
ca. $499 MSRP
No, Phi 4 reasoning vision 15B requires more memory than RTX 3070 Ti 8GB provides.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3070 Ti 8GB, Phi 4 reasoning vision 15B achieves approximately 13.1 tokens per second decode speed with a time-to-first-token of 14770ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on RTX 3070 Ti 8GB receives a F grade with 13.1 tok/s and 4K context.
On RTX 3070 Ti 8GB, Phi 4 reasoning vision 15B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
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