RTX 4000 Ada 20GB经济之选20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.7 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can Phi 4 reasoning vision 15B run on RTX 4080 Super 16GB?
YES — Tight Fit
C53Usable
Estimated from fit model
Phi 4 reasoning vision 15B needs ~13.4 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~68 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 13.4 GB, 68.3 tok/s, Tight fit
13.4 GB required16.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
68.3 tok/s
TTFT
2835 ms
Safe context
40K
Memory
13.4 GB / 16.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsPhi 4 reasoning vision 15B on RTX 4080 Super 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: 68.3 tok/s decode · 2.8s TTFT (warm) · 171 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 | B | Runs well | 68.3 tok/s | 1547 ms | 40K |
| Coding | C | Tight fit | 68.3 tok/s | 2835 ms | 40K |
| Agentic Coding | C | Tight fit | 68.3 tok/s | 4124 ms | 40K |
| Reasoning | C | Tight fit | 68.3 tok/s | 3351 ms | 40K |
| RAG | C | Tight fit | 68.3 tok/s | 5155 ms | 40K |
Inference speed
Phi 4 reasoning vision 15B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Phi 4 reasoning vision 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 131.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 83.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 75.5 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 71.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 68.3 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 60.9 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 50.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 48.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 26.7 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 24.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 15.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 5.9 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RTX 4080 Super 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升级选项
能流畅运行 Phi 4 reasoning vision 15B 的硬件
RTX 4000 Ada 20GB经济之选RTX 3090 24GB最佳性价比24 GB VRAM (+8)936 GB/s (+200)
C
Adds memory headroom for longer context windows and future model growth.68.3 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX 4090 24GBNVIDIA 升级24 GB VRAM (+8)1008 GB/s (+272)
C
Raises estimated decode speed by about 25%.85.6 tok/s 解码
Raises estimated decode speed by about 25%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Frequently asked questions
Can RTX 4080 Super 16GB run Phi 4 reasoning vision 15B?
Yes, RTX 4080 Super 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 68.3 tok/s.
How much VRAM does Phi 4 reasoning vision 15B need?
Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.4 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 4080 Super 16GB?
On RTX 4080 Super 16GB, Phi 4 reasoning vision 15B achieves approximately 68.3 tokens per second decode speed with a time-to-first-token of 2835ms using Q4_K_M quantization.
Can RTX 4080 Super 16GB run Phi 4 reasoning vision 15B for coding?
For coding workloads, Phi 4 reasoning vision 15B on RTX 4080 Super 16GB receives a C grade with 68.3 tok/s and 40K context.
What context window can Phi 4 reasoning vision 15B use on RTX 4080 Super 16GB?
On RTX 4080 Super 16GB, Phi 4 reasoning vision 15B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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