Can Phi 3.5 Mini 4B run on GTX 1060 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Phi 3.5 Mini 4B needs ~10.1 GB but GTX 1060 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: 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) — 10.1 GB, exceeds 6.0 GB available

10.1 GB required6.0 GB available

168% VRAM needed

4.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.8 tok/s

TTFT

17938 ms

Safe context

Memory

10.1 GB / 6.0 GB

Offload

40%

Memory breakdown

Weights2.4 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsPhi 3.5 Mini 4B on GTX 1060 6GB
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: 10.8 tok/s decode · 17.9s TTFT (warm) · 27 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 10.1 GB, but this setup only exposes 6.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	C	Very compromised	22.9 tok/s	4611 ms	5K
Coding	F	Too heavy	10.8 tok/s	17938 ms	5K
Agentic Coding	F	Too heavy	7.0 tok/s	40437 ms	5K
Reasoning	F	Too heavy	10.8 tok/s	21200 ms	5K
RAG	F	Too heavy	7.0 tok/s	50546 ms	5K

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on GTX 1060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B70
Q3_K_S	3	2.0 GB	Low	A70
NVFP4	4

Upgrade options

Hardware that runs Phi 3.5 Mini 4B well

RTX 3060 12GBBudget pick

12 GB VRAM (+6)360 GB/s (+168)

Makes the model fit on the accelerator instead of staying completely out of reach.56 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.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+10)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.56 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 (+10)288 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.56 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 GTX 1060 6GB run Phi 3.5 Mini 4B?

No, Phi 3.5 Mini 4B requires more memory than GTX 1060 6GB provides.

How much VRAM does Phi 3.5 Mini 4B need?

Phi 3.5 Mini 4B (4B parameters) requires approximately 10.1 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 GTX 1060 6GB?

On GTX 1060 6GB, Phi 3.5 Mini 4B achieves approximately 10.8 tokens per second decode speed with a time-to-first-token of 17938ms using Q4_K_M quantization.

Can GTX 1060 6GB run Phi 3.5 Mini 4B for coding?

For coding workloads, Phi 3.5 Mini 4B on GTX 1060 6GB receives a F grade with 10.8 tok/s and 5K context.

What context window can Phi 3.5 Mini 4B use on GTX 1060 6GB?

On GTX 1060 6GB, Phi 3.5 Mini 4B can safely use up to 5K 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 GTX 1060 6GB?

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 GTX 1060 6GB See all hardware for Phi 3.5 Mini 4B

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