RTX 3090 24GBコスパ重視24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.71.6 tok/sデコード
Raises estimated decode speed by about 133%.
〜$1,499 MSRP
Can Phi 4 reasoning vision 15B run on RTX 4000 Ada 20GB?
YES — Runs Great
C53Usable
Estimated from fit model
Phi 4 reasoning vision 15B needs ~14.1 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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) — 14.1 GB, 30.7 tok/s, Runs well
14.1 GB required20.0 GB available
71% VRAM used
Fit status
Runs well
Decode
30.7 tok/s
TTFT
6309 ms
Safe context
70K
Memory
14.1 GB / 20.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsPhi 4 reasoning vision 15B on RTX 4000 Ada 20GB
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: 30.7 tok/s decode · 6.3s TTFT (warm) · 77 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 | 30.7 tok/s | 3441 ms | 70K |
| Coding | C | Runs well | 30.7 tok/s | 6309 ms | 70K |
| Agentic Coding | C | Runs well | 30.7 tok/s | 9176 ms | 70K |
| Reasoning | C | Runs well | 30.7 tok/s | 7456 ms | 70K |
| RAG | C | Runs well | 30.7 tok/s | 11470 ms | 70K |
Quantization options
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C47 |
Q3_K_S | 3 | 7.4 GB | Low | C49 |
NVFP4 | 4 | 8.4 GB | Medium | C49 |
Q4_K_M | 4 | 9.2 GB | Medium | C50 |
Q5_K_M | 5 | 10.8 GB | High | C51 |
Q6_K | 6 | 12.3 GB | High | C50 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C50 |
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 3090 24GBコスパ重視RTX 4090 24GBコスパ最良24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 173%.83.7 tok/sデコード
Raises estimated decode speed by about 173%.
〜$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 101%.61.7 tok/sデコード
Raises estimated decode speed by about 101%.
〜$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run Phi 4 reasoning vision 15B?
Yes, RTX 4000 Ada 20GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 30.7 tok/s.
How much VRAM does Phi 4 reasoning vision 15B need?
Phi 4 reasoning vision 15B (15B parameters) requires approximately 14.1 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 RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Phi 4 reasoning vision 15B achieves approximately 30.7 tokens per second decode speed with a time-to-first-token of 6309ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run Phi 4 reasoning vision 15B for coding?
For coding workloads, Phi 4 reasoning vision 15B on RTX 4000 Ada 20GB receives a C grade with 30.7 tok/s and 70K context.
What context window can Phi 4 reasoning vision 15B use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Phi 4 reasoning vision 15B can safely use up to 70K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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