RTX 3060 12GBBest value12 GB VRAM (+2)
D
Makes the model fit on the accelerator instead of staying completely out of reach.15.7 tok/s decode
Makes the model fit on the accelerator instead of staying completely out of reach.
~$329 MSRP
Phi 4 reasoning vision 15B needs ~11.3 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q3_K_S quantization, expect ~42 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
3.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
26.8 tok/s
TTFT
7228 ms
Safe context
4K
Memory
13.1 GB / 10.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 31.0 tok/s | 3407 ms | 4K |
| Coding | F | Too heavy | 26.8 tok/s | 7228 ms | 4K |
| Agentic Coding | F | Too heavy | 20.6 tok/s | 13703 ms | 4K |
| Reasoning | F | Too heavy | 26.8 tok/s | 8543 ms | 4K |
| RAG | F | Too heavy | 20.6 tok/s | 17129 ms | 4K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.9 GB | Low | C52 |
Q3_K_S | 3 | 7.4 GB | Low | F0 |
Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.
Run
lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server startUpgrade options
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
~$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
Yes, RTX 3080 10GB can run Phi 4 reasoning vision 15B at Q3_K_S quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 13.1 GB which exceeds available memory, but at Q3_K_S it needs only 11.3 GB. Expected decode speed: 42.3 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.1 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at Q3_K_S using 11.3 GB.
The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is Q3_K_S, which uses 11.3 GB.
On RTX 3080 10GB, Phi 4 reasoning vision 15B achieves approximately 42.3 tokens per second decode speed with a time-to-first-token of 4575ms using Q3_K_S quantization.
For coding workloads, Phi 4 reasoning vision 15B on RTX 3080 10GB receives a F grade with 26.8 tok/s and 4K context.
On RTX 3080 10GB, Phi 4 reasoning vision 15B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, 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/hf-jamesburton--phi-4-reasoning-vision-15b-gguf-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.