Can Qwen 2.5 7B run on RTX 4060 Ti 8GB?

YES — Tight Fit

A78Great

Estimated from fit model

Qwen 2.5 7B needs ~7.1 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 7.1 GB, 53.5 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

53.5 tok/s

TTFT

3622 ms

Safe context

32K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 7B on RTX 4060 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: 53.5 tok/s decode · 3.6s TTFT (warm) · 134 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	Tight fit	53.5 tok/s	1975 ms	32K
Coding	A	Tight fit	53.5 tok/s	3622 ms	32K
Agentic Coding	A	Runs with offload	53.5 tok/s	5268 ms	32K
Reasoning	A	Tight fit	53.5 tok/s	4280 ms	32K
RAG	A	Runs with offload	53.5 tok/s	6585 ms	32K

Quantization options

How Qwen 2.5 7B (7B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A79
Q3_K_S	3	3.4 GB	Low	A79
NVFP4	4	3.9 GB	Medium	A79
Q4_K_M	4	4.3 GB	Medium	A79
Q5_K_MBest for your GPU	5	5.0 GB	High	A79
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 7B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your RTX 4060 Ti 8GB can run

Model	Params	Grade	Decode
Qwen 3 8B	8B	A	26.6 tok/s
Nemotron Nano 8B	8B	A	28.2 tok/s
InternVL2 8B	8B	A	28.2 tok/s
Ministral 3 8B	8B	A	26.6 tok/s
MiniCPM-V 2.6 8B	8B	A	28.2 tok/s

Frequently asked questions

Can RTX 4060 Ti 8GB run Qwen 2.5 7B?

Yes, RTX 4060 Ti 8GB can run Qwen 2.5 7B with a A grade (Tight fit). Expected decode speed: 53.5 tok/s.

How much VRAM does Qwen 2.5 7B need?

Qwen 2.5 7B (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 7B?

The recommended quantization for Qwen 2.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 7B run at on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, Qwen 2.5 7B achieves approximately 53.5 tokens per second decode speed with a time-to-first-token of 3622ms using Q4_K_M quantization.

Can RTX 4060 Ti 8GB run Qwen 2.5 7B for coding?

For coding workloads, Qwen 2.5 7B on RTX 4060 Ti 8GB receives a A grade with 53.5 tok/s and 32K context.

What context window can Qwen 2.5 7B use on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, Qwen 2.5 7B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 8GB See all hardware for Qwen 2.5 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-7b-on-rtx-4060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

53.5 tok/s

1975 ms

32K

Coding

Tight fit

53.5 tok/s

3622 ms

32K

Agentic Coding

Runs with offload

53.5 tok/s

5268 ms

32K

Reasoning

Tight fit

53.5 tok/s

4280 ms

32K

RAG

Runs with offload

53.5 tok/s

6585 ms

32K

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A79

Q3_K_S

3.4 GB

Low

A79

NVFP4

3.9 GB

Medium

A79

Q4_K_M

4.3 GB

Medium

A79

Q5_K_MBest for your GPU

5.0 GB

High

A79

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Model

Params

Grade

Decode

Capabilities

26.6 tok/s

28.2 tok/s

28.2 tok/s

26.6 tok/s

28.2 tok/s

Frequently asked questions

Can RTX 4060 Ti 8GB run Qwen 2.5 7B?

Yes, RTX 4060 Ti 8GB can run Qwen 2.5 7B with a A grade (Tight fit). Expected decode speed: 53.5 tok/s.

How much VRAM does Qwen 2.5 7B need?

Qwen 2.5 7B (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 7B?

The recommended quantization for Qwen 2.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 7B run at on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, Qwen 2.5 7B achieves approximately 53.5 tokens per second decode speed with a time-to-first-token of 3622ms using Q4_K_M quantization.

Can RTX 4060 Ti 8GB run Qwen 2.5 7B for coding?

For coding workloads, Qwen 2.5 7B on RTX 4060 Ti 8GB receives a A grade with 53.5 tok/s and 32K context.

What context window can Qwen 2.5 7B use on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, Qwen 2.5 7B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.