RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 81%.30.7 tok/sデコード
Raises estimated decode speed by about 81%.
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can Phi 4 reasoning vision 15B run on NVIDIA A2 16GB?
YES — Tight Fit
C48Usable
Estimated from fit model
Phi 4 reasoning vision 15B needs ~13.7 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~17 tok/s.
Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 13.7 GB, 17.0 tok/s, Tight fit
13.7 GB required16.0 GB available
86% VRAM used
Fit status
Tight fit
Decode
17.0 tok/s
TTFT
11355 ms
Safe context
37K
Memory
13.7 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsPhi 4 reasoning vision 15B on NVIDIA A2 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 17.0 tok/s | 6194 ms | 37K |
| Coding | C | Tight fit | 17.0 tok/s | 11355 ms | 37K |
| Agentic Coding | C | Runs with offload | 17.0 tok/s | 16517 ms | 37K |
| Reasoning | C | Tight fit | 17.0 tok/s | 13420 ms | 37K |
| RAG | C | Runs with offload | 17.0 tok/s | 20646 ms | 37K |
Quantization options
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C50 |
Q3_K_S | 3 | 7.4 GB | Low | C51 |
NVFP4 | 4 | 8.4 GB | Medium | C51 |
Q4_K_M | 4 | 9.2 GB | Medium | C51 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C50 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
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
Phi 4 reasoning vision 15Bを快適に動かすハードウェア
RTX 4000 Ada 20GBコスパ重視RTX 3090 24GBコスパ最良24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 321%.71.6 tok/sデコード
Raises estimated decode speed by about 321%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 263%.61.7 tok/sデコード
Raises estimated decode speed by about 263%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run Phi 4 reasoning vision 15B?
Yes, NVIDIA A2 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 17.0 tok/s.
How much VRAM does Phi 4 reasoning vision 15B need?
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 4 reasoning vision 15B?
The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 4 reasoning vision 15B run at on NVIDIA A2 16GB?
On NVIDIA A2 16GB, Phi 4 reasoning vision 15B achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11355ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run Phi 4 reasoning vision 15B for coding?
For coding workloads, Phi 4 reasoning vision 15B on NVIDIA A2 16GB receives a C grade with 17.0 tok/s and 37K context.
What context window can Phi 4 reasoning vision 15B use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, Phi 4 reasoning vision 15B can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-jamesburton--phi-4-reasoning-vision-15b-gguf-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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