MacBook Pro M4 Pro 24GBコスパ重視
24 GB Unified (+8)
A
This setup is broadly balanced for this model.56 tok/sデコード
〜$1,999 MSRP
Can Phi 4 Mini 4B run on RTX 5080 Laptop 16GB?
YES — Runs Great
B70Good
Estimated from fit model
Phi 4 Mini 4B needs ~6.7 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 6.7 GB, 56.0 tok/s, Runs well
6.7 GB required16.0 GB available
42% VRAM used
Fit status
Runs well
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
118K
Memory
6.7 GB / 16.0 GB
Memory breakdown
Weights2.4 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsPhi 4 Mini 4B on RTX 5080 Laptop 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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 | 56.0 tok/s | 1886 ms | 118K |
| Coding | B | Runs well | 56.0 tok/s | 3457 ms | 118K |
| Agentic Coding | A | Runs well | 56.0 tok/s | 5029 ms | 118K |
| Reasoning | B | Runs well | 56.0 tok/s | 4086 ms | 118K |
| RAG | A | Runs well | 56.0 tok/s | 6286 ms | 118K |
Inference speed
Phi 4 Mini 4B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Phi 4 Mini 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 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 | 76.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 64.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 64.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 64.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 56.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 56.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.0 | Fits |
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 Mini 4B (4B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B66 |
Q3_K_S | 3 | 2.0 GB | Low | B67 |
NVFP4 | 4 | 2.2 GB | Medium | B67 |
Q4_K_M | 4 | 2.4 GB | Medium | B67 |
Q5_K_M | 5 | 2.9 GB | High | B67 |
Q6_K | 6 | 3.3 GB | High | B68 |
Q8_0 | 8 | 4.3 GB | Very High | B69 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | A72 |
Get started
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniアップグレードオプション
Phi 4 Mini 4Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 5080 Laptop 16GB run Phi 4 Mini 4B?
Yes, RTX 5080 Laptop 16GB can run Phi 4 Mini 4B with a B grade (Runs well). Expected decode speed: 56.0 tok/s.
How much VRAM does Phi 4 Mini 4B need?
Phi 4 Mini 4B (4B parameters) requires approximately 6.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 4 Mini 4B?
The recommended quantization for Phi 4 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 4 Mini 4B run at on RTX 5080 Laptop 16GB?
On RTX 5080 Laptop 16GB, Phi 4 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
Can RTX 5080 Laptop 16GB run Phi 4 Mini 4B for coding?
For coding workloads, Phi 4 Mini 4B on RTX 5080 Laptop 16GB receives a B grade with 56.0 tok/s and 118K context.
What context window can Phi 4 Mini 4B use on RTX 5080 Laptop 16GB?
On RTX 5080 Laptop 16GB, Phi 4 Mini 4B can safely use up to 118K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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