Can Codestral Mamba 7B run on RTX 3050 Ti Laptop 4GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Codestral Mamba 7B needs ~6.4 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) — 6.4 GB, exceeds 4.0 GB available

6.4 GB required4.0 GB available

160% VRAM needed

2.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.9 tok/s

TTFT

19524 ms

Safe context

Memory

6.4 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral Mamba 7B 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: 9.9 tok/s decode · 19.5s TTFT (warm) · 25 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.4 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	10.8 tok/s	9807 ms	4K
Coding	F	Too heavy	9.9 tok/s	19524 ms	4K
Agentic Coding	F	Too heavy	8.5 tok/s	33185 ms	4K
Reasoning	F	Too heavy	9.9 tok/s	23074 ms	4K
RAG	F	Too heavy	8.5 tok/s	41481 ms	4K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	F0
Q3_K_S	3	3.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Codestral Mamba 7B well

👁 NVIDIA
GTX 1660 Super 6GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.26 tok/s decode

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

Raises estimated decode speed by about 163%.

~$229 MSRP

👁 NVIDIA
RTX 3050 8GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.34.6 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

👁 NVIDIA
GTX 1060 6GBNVIDIA upgrade

6 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.15.9 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

👁 NVIDIA
RTX 3080 10GBBiggest leap

10 GB VRAM (+6)760 GB/s (+568)

Makes the model fit on the accelerator instead of staying completely out of reach.98 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.

~$699 MSRP

Frequently asked questions

See all results for RTX 3050 Ti Laptop 4GB See all hardware for Codestral Mamba 7B

URL: https://willitrunai.com/can-run/codestral-mamba-7b-on-rtx-3050-ti-laptop-4gb

⇱

Can Codestral Mamba 7B run on RTX 3050 Ti Laptop 4GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Codestral Mamba 7B well

Frequently asked questions