Phi 4 reasoning vision 15B needs ~13.0 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~16 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
1.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.7 GB host RAM)
Decode
15.5 tok/s
TTFT
12460 ms
Safe context
7K
Memory
13.0 GB / 12.0 GB
Offload
10%
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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.1 GB host RAM) | 18.0 tok/s | 5865 ms | 7K |
| Coding | D | Very compromised (needs ~0.7 GB host RAM) | 15.5 tok/s | 12460 ms | 7K |
| Agentic Coding | F | Too heavy | 11.9 tok/s | 23665 ms | 7K |
| Reasoning | D | Very compromised (needs ~0.7 GB host RAM) | 15.5 tok/s | 14725 ms | 7K |
| RAG | F | Too heavy | 11.9 tok/s | 29581 ms | 7K |
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C52 |
Q3_K_S | 3 | 7.4 GB | Low | C52 |
NVFP4Best for your GPU | 4 | 8.4 GB | Medium | C51 |
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 |
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 startアップグレードオプション
RTX 5060 Ti 16GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 85%.
〜$449 MSRP
RTX 4060 Ti 16GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 52%.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレードRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 54%.
〜$625 MSRP
Yes, RTX A2000 12GB can run Phi 4 reasoning vision 15B with a D grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 15.5 tok/s.
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.0 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 A2000 12GB, Phi 4 reasoning vision 15B achieves approximately 15.5 tokens per second decode speed with a time-to-first-token of 12460ms using Q4_K_M quantization.
For coding workloads, Phi 4 reasoning vision 15B on RTX A2000 12GB receives a D grade with 15.5 tok/s and 7K context.
On RTX A2000 12GB, Phi 4 reasoning vision 15B can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
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