VOOZH about

URL: https://willitrunai.com/can-run/phi-4-reasoning-plus-14b-on-a2000-12gb


Can Phi-4-reasoning-plus 14B run on RTX A2000 12GB?

BARELY — Tight on Memory

A71Great
Estimated from fit model

Phi-4-reasoning-plus 14B needs ~14.1 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
Share:

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) — 14.1 GB, 14.3 tok/s, Very compromised (needs ~1.3 GB host RAM)
14.1 GB required12.0 GB available
118% VRAM needed

2.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

14.3 tok/s

TTFT

13497 ms

Safe context

5K

Memory

14.1 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights9.0 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on RTX A2000 12GB
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: 14.3 tok/s decode · 13.5s TTFT (warm) · 36 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns with offload17.0 tok/s6221 ms5K
CodingAVery compromised13.3 tok/s14509 ms5K
Agentic CodingFToo heavy8.8 tok/s31862 ms5K
ReasoningAVery compromised13.3 tok/s17147 ms5K
RAGFToo heavy8.8 tok/s39828 ms5K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.7 GB
LowS92
Q3_K_S
3
7.2 GB
LowS92
NVFP4Best for your GPU

Get started

Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.

Run

ollama run phi4-reasoning

Frequently asked questions

See all results for RTX A2000 12GBSee all hardware for Phi-4-reasoning-plus 14B
4
8.2 GB
Medium
S91
Q4_K_M
4
9.0 GB
MediumF0
Q5_K_M
5
10.6 GB
HighF0
Q6_K
6
12.1 GB
HighF0
Q8_0
8
15.7 GB
Very HighF0
F16
16
30.1 GB
MaximumF0

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.