Can Phi 4 Mini 4B run on NVIDIA A800 80GB?

YES — Runs Great

B64Good

Estimated from fit model

Phi 4 Mini 4B needs ~13.1 GB VRAM. NVIDIA A800 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.

Capabilities:

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 A800 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 A800 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

Get started

Copy-paste commands to run Phi 4 Mini 4B on your machine.

Run

ollama run phi4-mini

Upgrade options

Hardware that runs Phi 4 Mini 4B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.56 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.56 tok/s decode

~$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.56 tok/s decode

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can NVIDIA A800 80GB run Phi 4 Mini 4B?

Yes, NVIDIA A800 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 A800 80GB?

On NVIDIA A800 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 A800 80GB run Phi 4 Mini 4B for coding?

For coding workloads, Phi 4 Mini 4B on NVIDIA A800 80GB receives a B grade with 56.0 tok/s and 128K context.

What context window can Phi 4 Mini 4B use on NVIDIA A800 80GB?

On NVIDIA A800 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.

See all results for NVIDIA A800 80GB See all hardware for Phi 4 Mini 4B

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