MacBook Pro M3 Max 128GBコスパ重視
128 GB Unified (+48)
B
This setup is broadly balanced for this model.56 tok/sデコード
〜$2,499 MSRP
Can Phi 4 Mini 4B run on NVIDIA A100 80GB?
YES — Runs Great
B64Good
Estimated from fit model
Phi 4 Mini 4B needs ~13.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 13.1 GB, 56.0 tok/s, Runs well
13.1 GB required80.0 GB available
16% VRAM used
Fit status
Runs well
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
128K
Memory
13.1 GB / 80.0 GB
Memory breakdown
Weights2.4 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsPhi 4 Mini 4B on NVIDIA A100 80GB
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 | 128K |
| Coding | B | Runs well | 56.0 tok/s | 3457 ms | 128K |
| Agentic Coding | B | Runs well | 56.0 tok/s | 5029 ms | 128K |
| Reasoning | B | Runs well | 56.0 tok/s | 4086 ms | 128K |
| RAG | B | Runs well | 56.0 tok/s | 6286 ms | 128K |
Quantization options
How Phi 4 Mini 4B (4B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B60 |
Q3_K_S | 3 | 2.0 GB | Low | B60 |
NVFP4 | 4 | 2.2 GB | Medium | B60 |
Q4_K_M | 4 | 2.4 GB | Medium | B60 |
Q5_K_M | 5 | 2.9 GB | High | B60 |
Q6_K | 6 | 3.3 GB | High | B60 |
Q8_0 | 8 | 4.3 GB | Very High | B60 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B61 |
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
Mac Studio M2 Ultra 128GBコスパ最良
128 GB Unified (+48)
B
This setup is broadly balanced for this model.56 tok/sデコード
〜$3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIAアップグレード128 GB Unified (+48)
B
Adds memory headroom for longer context windows and future model growth.56 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
Frequently asked questions
Can NVIDIA A100 80GB run Phi 4 Mini 4B?
Yes, NVIDIA A100 80GB 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 13.1 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 NVIDIA A100 80GB?
On NVIDIA A100 80GB, 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 NVIDIA A100 80GB run Phi 4 Mini 4B for coding?
For coding workloads, Phi 4 Mini 4B on NVIDIA A100 80GB receives a B grade with 56.0 tok/s and 128K context.
What context window can Phi 4 Mini 4B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Phi 4 Mini 4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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