Can Qwen 3.5 4B run on RTX 3050 Ti Laptop 4GB?

Q: What should I upgrade first if Qwen 3.5 4B feels slow on RTX 3050 Ti Laptop 4GB?

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.

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3.5 4B needs ~6.2 GB but RTX 3050 Ti Laptop 4GB only has 4.0 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) — 6.2 GB, exceeds 4.0 GB available

6.2 GB required4.0 GB available

155% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

19.4 tok/s

TTFT

9967 ms

Safe context

Memory

6.2 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 4B on RTX 3050 Ti Laptop 4GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 19.4 tok/s decode · 10.0s TTFT (warm) · 49 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.2 GB, but this setup only exposes 4.0 GB of usable VRAM.

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	F	Too heavy	29.2 tok/s	3616 ms	4K
Coding	F	Too heavy	19.4 tok/s	9967 ms	4K
Agentic Coding	F	Too heavy	10.3 tok/s	27364 ms	4K
Reasoning	F	Too heavy	19.4 tok/s	11779 ms	4K
RAG	F	Too heavy	10.3 tok/s	34205 ms	4K

Quantization options

How Qwen 3.5 4B (4B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	1.6 GB	Low	S95
Q3_K_S	3	2.0 GB	Low	F0

Upgrade options

Hardware that runs Qwen 3.5 4B well

GTX 1660 Super 6GBBudget pick

6 GB VRAM (+2)336 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.50.9 tok/s decode

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

Raises estimated decode speed by about 162%.

~$229 MSRP

RTX 3050 8GBBest value

8 GB VRAM (+4)224 GB/s (+32)

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.

~$249 MSRP

GTX 1060 6GBNVIDIA upgrade

6 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.31.2 tok/s decode

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

Raises estimated decode speed by about 61%.

~$249 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run Qwen 3.5 4B?

No, Qwen 3.5 4B requires more memory than RTX 3050 Ti Laptop 4GB provides.

How much VRAM does Qwen 3.5 4B need?

Qwen 3.5 4B (4B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 4B?

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

What speed will Qwen 3.5 4B run at on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Qwen 3.5 4B achieves approximately 19.4 tokens per second decode speed with a time-to-first-token of 9967ms using Q4_K_M quantization.

Can RTX 3050 Ti Laptop 4GB run Qwen 3.5 4B for coding?

For coding workloads, Qwen 3.5 4B on RTX 3050 Ti Laptop 4GB receives a F grade with 19.4 tok/s and 4K context.

What context window can Qwen 3.5 4B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Qwen 3.5 4B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 4B feels slow on RTX 3050 Ti Laptop 4GB?

See all results for RTX 3050 Ti Laptop 4GB See all hardware for Qwen 3.5 4B

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