Can Phi 3.5 Mini 4B run on RTX 5060 Ti 8GB?

YES — With Q2_K

C52Usable

Estimated from fit model

Phi 3.5 Mini 4B needs ~9.4 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q2_K quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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.

Phi 3.5 Mini 4B at Q4_K_M needs 10.3 GB — too much for RTX 5060 Ti 8GB (8.0 GB). Runs at Q2_K (9.4 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.3 GB, exceeds 8.0 GB available

10.3 GB required8.0 GB available

129% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

52.0 tok/s

TTFT

3724 ms

Safe context

10K

Memory

10.3 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights2.4 GB

KV Cache5.9 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 3.5 Mini 4B on RTX 5060 Ti 8GB
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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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 20% 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 0.2 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	B	Tight fit	56.0 tok/s	1886 ms	10K
Coding	F	Too heavy	52.0 tok/s	3724 ms	10K
Agentic Coding	F	Too heavy	20.7 tok/s	13574 ms	10K
Reasoning	F	Too heavy	52.0 tok/s	4401 ms	10K
RAG	F	Too heavy	20.7 tok/s	16967 ms	10K

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B67
Q3_K_S	3	2.0 GB	Low	B67
NVFP4	4	2.2 GB	Medium	B68
Q4_K_M	4	2.4 GB	Medium	B68
Q5_K_M	5	2.9 GB	High	B69
Q6_K	6	3.3 GB	High	B69
Q8_0Best for your GPU	8	4.3 GB	Very High	B69
F16	16	8.2 GB	Maximum	F0

Get started

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

Run

ollama run phi3.5

升级选项

能流畅运行 Phi 3.5 Mini 4B 的硬件

RTX 3060 12GB经济之选

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/s 解码

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.

~$329 MSRP

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/s 解码

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 升级

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/s 解码

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 5060 Ti 8GB run Phi 3.5 Mini 4B?

Yes, RTX 5060 Ti 8GB can run Phi 3.5 Mini 4B at Q2_K quantization (Very compromised (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 10.3 GB which exceeds available memory, but at Q2_K it needs only 9.4 GB. 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 10.3 GB at Q4_K_M quantization. On RTX 5060 Ti 8GB, it fits at Q2_K using 9.4 GB.

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

The recommended quantization is Q4_K_M, but on RTX 5060 Ti 8GB the best fitting quantization is Q2_K, which uses 9.4 GB.

What speed will Phi 3.5 Mini 4B run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q2_K quantization.

Can RTX 5060 Ti 8GB run Phi 3.5 Mini 4B for coding?

For coding workloads, Phi 3.5 Mini 4B on RTX 5060 Ti 8GB receives a F grade with 52.0 tok/s and 10K context.

What context window can Phi 3.5 Mini 4B use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Phi 3.5 Mini 4B can safely use up to 12K tokens of context at Q2_K quantization. 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 5060 Ti 8GB?

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 5060 Ti 8GB See all hardware for Phi 3.5 Mini 4B

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