Can Phi-4 14B run on RTX 3070 Ti 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Phi-4 14B needs ~13.6 GB but RTX 3070 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 13.6 GB, exceeds 8.0 GB available

13.6 GB required8.0 GB available

170% VRAM needed

5.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.5 tok/s

TTFT

14295 ms

Safe context

Memory

13.6 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights8.5 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsPhi-4 14B on RTX 3070 Ti 8GB
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: 13.5 tok/s decode · 14.3s TTFT (warm) · 34 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.6 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	17.4 tok/s	6069 ms	4K
Coding	F	Too heavy	12.6 tok/s	15368 ms	4K
Agentic Coding	F	Too heavy	8.8 tok/s	31849 ms	4K
Reasoning	F	Too heavy	13.5 tok/s	16895 ms	4K
RAG	F	Too heavy	8.8 tok/s	39812 ms	4K

Quantization options

How Phi-4 14B (14B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Phi-4 14B well

RTX 3060 12GBBest value

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.16.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$329 MSRP

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.35 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.26.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 3070 Ti 8GB run Phi-4 14B?

No, Phi-4 14B requires more memory than RTX 3070 Ti 8GB provides.

How much VRAM does Phi-4 14B need?

Phi-4 14B (14B parameters) requires approximately 13.6 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 3070 Ti 8GB?

On RTX 3070 Ti 8GB, Phi-4 14B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15368ms using Q4_K_M quantization.

Can RTX 3070 Ti 8GB run Phi-4 14B for coding?

For coding workloads, Phi-4 14B on RTX 3070 Ti 8GB receives a F grade with 12.6 tok/s and 4K context.

What context window can Phi-4 14B use on RTX 3070 Ti 8GB?

On RTX 3070 Ti 8GB, Phi-4 14B can safely use up to 4K 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 3070 Ti 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 3070 Ti 8GB See all hardware for Phi-4 14B

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