Can Mamba Codestral 7B v0.1 run on GTX 1650 4GB?

Q: What should I upgrade first if Mamba Codestral 7B v0.1 feels slow on GTX 1650 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

Mamba Codestral 7B v0.1 needs ~6.7 GB but GTX 1650 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) — 6.7 GB, exceeds 4.0 GB available

6.7 GB required4.0 GB available

168% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.5 tok/s

TTFT

54795 ms

Safe context

Memory

6.7 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMamba Codestral 7B v0.1 on GTX 1650 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: 3.5 tok/s decode · 54.8s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.7 GB, but this setup only exposes 4.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	4.1 tok/s	26011 ms	4K
Coding	F	Too heavy	3.5 tok/s	54795 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	102747 ms	4K
Reasoning	F	Too heavy	3.5 tok/s	64757 ms	4K
RAG	F	Too heavy	2.7 tok/s	128434 ms	4K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on GTX 1650 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 Mamba Codestral 7B v0.1 well

GTX 1660 Super 6GBNVIDIA upgrade

6 GB VRAM (+2)336 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.23.3 tok/s decode

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

Raises estimated decode speed by about 566%.

~$229 MSRP

RTX 3050 8GBBudget pick

8 GB VRAM (+4)224 GB/s (+96)

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

RTX 5060 8GBBest value

8 GB VRAM (+4)448 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.64 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.

~$299 MSRP

Frequently asked questions

Can GTX 1650 4GB run Mamba Codestral 7B v0.1?

No, Mamba Codestral 7B v0.1 requires more memory than GTX 1650 4GB provides.

How much VRAM does Mamba Codestral 7B v0.1 need?

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 6.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mamba Codestral 7B v0.1?

The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mamba Codestral 7B v0.1 run at on GTX 1650 4GB?

On GTX 1650 4GB, Mamba Codestral 7B v0.1 achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 54795ms using Q4_K_M quantization.

Can GTX 1650 4GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on GTX 1650 4GB receives a F grade with 3.5 tok/s and 4K context.

What context window can Mamba Codestral 7B v0.1 use on GTX 1650 4GB?

On GTX 1650 4GB, Mamba Codestral 7B v0.1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Mamba Codestral 7B v0.1 feels slow on GTX 1650 4GB?

See all results for GTX 1650 4GB See all hardware for Mamba Codestral 7B v0.1

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