Can Qwen 3.5 4B run on RTX 5050 8GB?

YES — Runs Great

S96Excellent

○Estimated from fit model

Qwen 3.5 4B needs ~6.3 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 6.3 GB, 76.0 tok/s, Runs well

6.3 GB required8.0 GB available

79% VRAM used

Fit status

Runs well

Decode

76.0 tok/s

TTFT

2547 ms

Safe context

28K

Memory

6.3 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 4B on RTX 5050 8GB
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: 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	S	Runs well	76.0 tok/s	1389 ms	28K
Coding	S	Runs well	76.0 tok/s	2547 ms	28K
Agentic Coding	A	Runs with offload (needs ~0.2 GB host RAM)	55.6 tok/s	5069 ms	28K
Reasoning	S	Runs well	76.0 tok/s	3011 ms	28K
RAG	A	Runs with offload (needs ~0.2 GB host RAM)	55.6 tok/s	6336 ms	28K

Quantization options

How Qwen 3.5 4B (4B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	S91
Q3_K_S	3	2.0 GB	Low	S92
NVFP4	4	2.2 GB	Medium	S92
Q4_K_M	4	2.4 GB	Medium	S93
Q5_K_M	5	2.9 GB	High	S94
Q6_K	6	3.3 GB	High	S94
Q8_0Best for your GPU	8	4.3 GB	Very High	S93
F16	16	8.2 GB	Maximum	F0

Get started

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

Run

ollama run qwen3.5:4b

Your hardware

More models your RTX 5050 8GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	A	20.3 tok/s

Frequently asked questions

See all results for RTX 5050 8GB See all hardware for Qwen 3.5 4B

URL: https://willitrunai.com/can-run/qwen-3.5-4b-on-rtx-5050-8gb