Can Phi 3.5 Mini 4B run on RTX 3080 10GB?

Q: What should I upgrade first if Phi 3.5 Mini 4B feels slow on RTX 3080 10GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

B68Good

Estimated from fit model

Phi 3.5 Mini 4B needs ~10.5 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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) — 10.5 GB, 56.0 tok/s, Runs with offload (needs ~0.1 GB host RAM)

10.5 GB required10.0 GB available

105% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.1 GB host RAM)

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

15K

Memory

10.5 GB / 10.0 GB

Memory breakdown

Weights2.4 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsPhi 3.5 Mini 4B on RTX 3080 10GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	56.0 tok/s	1886 ms	15K
Coding	B	Runs with offload (needs ~0.1 GB host RAM)	56.0 tok/s	3457 ms	15K
Agentic Coding	F	Too heavy	56.0 tok/s	5029 ms	15K
Reasoning	B	Runs with offload (needs ~0.1 GB host RAM)	56.0 tok/s	4086 ms	15K
RAG	F	Too heavy	56.0 tok/s	6286 ms

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B65
Q3_K_S	3	2.0 GB	Low	B65
NVFP4	4

Get started

Copy-paste commands to run Phi 3.5 Mini 4B on your machine.

Run

ollama run phi3.5

Upgrade options

Hardware that runs Phi 3.5 Mini 4B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+6)

Adds memory headroom for longer context windows and future model growth.56 tok/s decode

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

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+6)

Adds memory headroom for longer context windows and future model growth.56 tok/s decode

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

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+6)

Adds memory headroom for longer context windows and future model growth.56 tok/s decode

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

~$625 MSRP

Frequently asked questions

Can RTX 3080 10GB run Phi 3.5 Mini 4B?

Yes, RTX 3080 10GB can run Phi 3.5 Mini 4B with a B grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 56.0 tok/s.

How much VRAM does Phi 3.5 Mini 4B need?

Phi 3.5 Mini 4B (4B parameters) requires approximately 10.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3.5 Mini 4B?

The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3.5 Mini 4B run at on RTX 3080 10GB?

On RTX 3080 10GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can RTX 3080 10GB run Phi 3.5 Mini 4B for coding?

For coding workloads, Phi 3.5 Mini 4B on RTX 3080 10GB receives a B grade with 56.0 tok/s and 15K context.

What context window can Phi 3.5 Mini 4B use on RTX 3080 10GB?

On RTX 3080 10GB, Phi 3.5 Mini 4B can safely use up to 15K 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 3.5 Mini 4B feels slow on RTX 3080 10GB?

See all results for RTX 3080 10GB See all hardware for Phi 3.5 Mini 4B

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