Will It Run AI

Can Phi 3 Medium 14B run on RTX 4070 Super 12GB?

BARELY — Tight on Memory

C51Usable
Estimated from fit model

Phi 3 Medium 14B needs ~13.7 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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, 29.1 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

29.1 tok/s

TTFT

6643 ms

Safe context

7K

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 3 Medium 14B on RTX 4070 Super 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: 29.1 tok/s decode · 6.6s TTFT (warm) · 73 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload (needs ~0.1 GB host RAM)37.4 tok/s2825 ms7K
CodingCVery compromised (needs ~1.1 GB host RAM)29.1 tok/s6643 ms7K
Agentic CodingFToo heavy19.1 tok/s14758 ms7K
ReasoningCVery compromised (needs ~1.1 GB host RAM)29.1 tok/s7851 ms7K
RAGFToo heavy19.1 tok/s18448 ms7K

Quantization options

How Phi 3 Medium 14B (14B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowB63
Q3_K_S
3
6.9 GB
LowB63
NVFP4
4
7.8 GB
MediumB63
Q4_K_MBest for your GPU
4
8.5 GB
MediumB63
Q5_K_M
5
10.1 GB
HighF0
Q6_K
6
11.5 GB
HighF0
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

Get started

Copy-paste commands to run Phi 3 Medium 14B on your machine.

Run

ollama run phi3:medium

Opciones de mejora

Hardware que ejecuta bien Phi 3 Medium 14B

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+4)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.33.9 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+4)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.27.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

NVIDIARTX 2000 Ada 16GBMejora NVIDIA
16 GB VRAM (+4)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.27.6 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$625 MSRP

NVIDIARTX A4500 20GBMayor salto
20 GB VRAM (+8)640 GB/s (+136)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.62.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 116%.

~$2,000 MSRP

Frequently asked questions

Can RTX 4070 Super 12GB run Phi 3 Medium 14B?

Yes, RTX 4070 Super 12GB can run Phi 3 Medium 14B with a C grade (Very compromised (needs ~1.1 GB host RAM)). Expected decode speed: 29.1 tok/s.

How much VRAM does Phi 3 Medium 14B need?

Phi 3 Medium 14B (14B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Medium 14B?

The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Medium 14B run at on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, Phi 3 Medium 14B achieves approximately 29.1 tokens per second decode speed with a time-to-first-token of 6643ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run Phi 3 Medium 14B for coding?

For coding workloads, Phi 3 Medium 14B on RTX 4070 Super 12GB receives a C grade with 29.1 tok/s and 7K context.

What context window can Phi 3 Medium 14B use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, Phi 3 Medium 14B can safely use up to 7K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Phi 3 Medium 14B feels slow on RTX 4070 Super 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 Super 12GBSee all hardware for Phi 3 Medium 14B
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