Can Phi 3 Mini 3.8B run on RTX 4070 12GB?

YES — Tight Fit

B70Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~10.3 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~61 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 10.3 GB, 60.8 tok/s, Tight fit

10.3 GB required12.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

60.8 tok/s

TTFT

3184 ms

Safe context

21K

Memory

10.3 GB / 12.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on RTX 4070 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 60.8 tok/s decode · 3.2s TTFT (warm) · 152 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	A	Runs well	60.8 tok/s	1737 ms	21K
Coding	B	Tight fit	60.8 tok/s	3184 ms	21K
Agentic Coding	F	Too heavy	60.3 tok/s	4669 ms	21K
Reasoning	B	Tight fit	60.8 tok/s	3763 ms	21K
RAG	F	Too heavy	60.3 tok/s	5836 ms	21K

Inference speed

Phi 3 Mini 3.8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Mini 3.8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	72.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	60.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	60.8

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B65
Q3_K_S	3	1.9 GB	Low	B65
NVFP4	4

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

Upgrade options

Hardware that runs Phi 3 Mini 3.8B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.72.2 tok/s decode

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

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.60.8 tok/s decode

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

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.53.2 tok/s decode

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

~$625 MSRP

Frequently asked questions

Can RTX 4070 12GB run Phi 3 Mini 3.8B?

Yes, RTX 4070 12GB can run Phi 3 Mini 3.8B with a B grade (Tight fit). Expected decode speed: 60.8 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on RTX 4070 12GB?

On RTX 4070 12GB, Phi 3 Mini 3.8B achieves approximately 60.8 tokens per second decode speed with a time-to-first-token of 3184ms using Q4_K_M quantization.

Can RTX 4070 12GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on RTX 4070 12GB receives a B grade with 60.8 tok/s and 21K context.

What context window can Phi 3 Mini 3.8B use on RTX 4070 12GB?

On RTX 4070 12GB, Phi 3 Mini 3.8B can safely use up to 21K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4070 12GB See all hardware for Phi 3 Mini 3.8B

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