Can Mamba Codestral 7B v0.1 run on RTX 2080 Ti 11GB?

YES — Runs Great

B56Good

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~7.4 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~94 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 7.4 GB, 93.8 tok/s, Runs well

7.4 GB required11.0 GB available

67% VRAM used

Fit status

Runs well

Decode

93.8 tok/s

TTFT

2065 ms

Safe context

86K

Memory

7.4 GB / 11.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on RTX 2080 Ti 11GB
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: 93.8 tok/s decode · 2.1s TTFT (warm) · 234 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	93.8 tok/s	1126 ms	86K
Coding	B	Runs well	93.8 tok/s	2065 ms	86K
Agentic Coding	B	Runs well	93.8 tok/s	3003 ms	86K
Reasoning	B	Runs well	93.8 tok/s	2440 ms	86K
RAG	B	Runs well	93.8 tok/s	3754 ms	86K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run Mamba Codestral 7B v0.1 on your machine.

Run

lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server start

Frequently asked questions

Can RTX 2080 Ti 11GB run Mamba Codestral 7B v0.1?

Yes, RTX 2080 Ti 11GB can run Mamba Codestral 7B v0.1 with a B grade (Runs well). Expected decode speed: 93.8 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 7.4 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 RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Mamba Codestral 7B v0.1 achieves approximately 93.8 tokens per second decode speed with a time-to-first-token of 2065ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 2080 Ti 11GB receives a B grade with 93.8 tok/s and 86K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Mamba Codestral 7B v0.1 can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2080 Ti 11GB See all hardware for Mamba Codestral 7B v0.1

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