Can Phi-4 14B run on RTX 4070 12GB?

BARELY — Tight on Memory

A72Great

Estimated from fit model

Phi-4 14B needs ~13.7 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 13.7 GB, 28.4 tok/s, Very compromised (needs ~1.1 GB host RAM)

13.7 GB required12.0 GB available

114% VRAM needed

1.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.1 GB host RAM)

Decode

28.4 tok/s

TTFT

6820 ms

Safe context

Memory

13.7 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache3.1 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi-4 14B on RTX 4070 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: 28.4 tok/s decode · 6.8s TTFT (warm) · 71 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 10% 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 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.1 GB host RAM)	36.4 tok/s	2901 ms	7K
Coding	A	Very compromised (needs ~1.1 GB host RAM)	28.4 tok/s	6820 ms	7K
Agentic Coding	F	Too heavy	18.6 tok/s	15152 ms	7K
Reasoning	A	Very compromised (needs ~1.1 GB host RAM)	28.4 tok/s	8060 ms	7K
RAG	F	Too heavy	18.6 tok/s	18940 ms	7K

Quantization options

How Phi-4 14B (14B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A84
Q3_K_S	3	6.9 GB	Low	A84
NVFP4	4	7.8 GB	Medium	A83
Q4_K_MBest for your GPU	4	8.5 GB	Medium	A83
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

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

Run

ollama run phi4

Your hardware

More models your RTX 4070 12GB can run

Model	Params	Grade	Decode	Capabilities
Phi-4-reasoning-plus 14B	14.7B	A	24.9 tok/s

Frequently asked questions

Can RTX 4070 12GB run Phi-4 14B?

Yes, RTX 4070 12GB can run Phi-4 14B with a A grade (Very compromised (needs ~1.1 GB host RAM)). Expected decode speed: 28.4 tok/s.

How much VRAM does Phi-4 14B need?

Phi-4 14B (14B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4 14B?

The recommended quantization for Phi-4 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4 14B run at on RTX 4070 12GB?

On RTX 4070 12GB, Phi-4 14B achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6820ms using Q4_K_M quantization.

Can RTX 4070 12GB run Phi-4 14B for coding?

For coding workloads, Phi-4 14B on RTX 4070 12GB receives a A grade with 28.4 tok/s and 7K context.

What context window can Phi-4 14B use on RTX 4070 12GB?

On RTX 4070 12GB, Phi-4 14B can safely use up to 7K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if Phi-4 14B feels slow on RTX 4070 12GB?

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.

See all results for RTX 4070 12GB See all hardware for Phi-4 14B

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