Can Qwen3.5 4B run on RTX 5060 Ti 8GB?

YES — Runs Great

C54Usable

Estimated from fit model

Qwen3.5 4B needs ~4.6 GB VRAM. RTX 5060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 4.6 GB, 76.0 tok/s, Runs well

4.6 GB required8.0 GB available

57% VRAM used

Fit status

Runs well

Decode

76.0 tok/s

TTFT

2547 ms

Safe context

132K

Memory

4.6 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen3.5 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: 76.0 tok/s decode · 2.5s TTFT (warm) · 190 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	C	Runs well	76.0 tok/s	1389 ms	132K
Coding	C	Runs well	76.0 tok/s	2547 ms	132K
Agentic Coding	B	Runs well	76.0 tok/s	3705 ms	132K
Reasoning	C	Runs well	76.0 tok/s	3011 ms	132K
RAG	B	Runs well	76.0 tok/s	4632 ms	132K

Quantization options

How Qwen3.5 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	C51
Q3_K_S	3	2.0 GB	Low	C52
NVFP4	4	2.2 GB	Medium	C52
Q4_K_M	4	2.4 GB	Medium	C53
Q5_K_M	5	2.9 GB	High	C54
Q6_K	6	3.3 GB	High	C54
Q8_0Best for your GPU	8	4.3 GB	Very High	C53
F16	16	8.2 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3.5 4B on your machine.

Run

lms load hf-unsloth--qwen3-5-4b-gguf && lms server start

Frequently asked questions

Can RTX 5060 Ti 8GB run Qwen3.5 4B?

Yes, RTX 5060 Ti 8GB can run Qwen3.5 4B with a C grade (Runs well). Expected decode speed: 76.0 tok/s.

How much VRAM does Qwen3.5 4B need?

Qwen3.5 4B (4B parameters) requires approximately 4.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 4B?

The recommended quantization for Qwen3.5 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 4B run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Qwen3.5 4B achieves approximately 76.0 tokens per second decode speed with a time-to-first-token of 2547ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run Qwen3.5 4B for coding?

For coding workloads, Qwen3.5 4B on RTX 5060 Ti 8GB receives a C grade with 76.0 tok/s and 132K context.

What context window can Qwen3.5 4B use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Qwen3.5 4B can safely use up to 132K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5060 Ti 8GB See all hardware for Qwen3.5 4B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-4b-gguf-on-rtx-5060-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

Runs well

76.0 tok/s

1389 ms

132K

Coding

Runs well

76.0 tok/s

2547 ms

132K

Agentic Coding

Runs well

76.0 tok/s

3705 ms

132K

Reasoning

Runs well

76.0 tok/s

3011 ms

132K

RAG

Runs well

76.0 tok/s

4632 ms

132K

Quant

Bits

VRAM

Quality

Fit

Q2_K

1.6 GB

Low

C51

Q3_K_S

2.0 GB

Low

C52

NVFP4

2.2 GB

Medium

C52

Q4_K_M

2.4 GB

Medium

C53

Q5_K_M

2.9 GB

High

C54

Q6_K

3.3 GB

High

C54

Q8_0Best for your GPU

4.3 GB

Very High

C53

F16

8.2 GB

Maximum

Frequently asked questions

Can RTX 5060 Ti 8GB run Qwen3.5 4B?

Yes, RTX 5060 Ti 8GB can run Qwen3.5 4B with a C grade (Runs well). Expected decode speed: 76.0 tok/s.

How much VRAM does Qwen3.5 4B need?

Qwen3.5 4B (4B parameters) requires approximately 4.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 4B?

The recommended quantization for Qwen3.5 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 4B run at on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Qwen3.5 4B achieves approximately 76.0 tokens per second decode speed with a time-to-first-token of 2547ms using Q4_K_M quantization.

Can RTX 5060 Ti 8GB run Qwen3.5 4B for coding?

For coding workloads, Qwen3.5 4B on RTX 5060 Ti 8GB receives a C grade with 76.0 tok/s and 132K context.

What context window can Qwen3.5 4B use on RTX 5060 Ti 8GB?

On RTX 5060 Ti 8GB, Qwen3.5 4B can safely use up to 132K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.