Can Mamba Codestral 7B v0.1 run on RTX 2060 Super 8GB?

YES — Tight Fit

C53Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 6.8 GB, 70.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

70.0 tok/s

TTFT

2766 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on RTX 2060 Super 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: 70.0 tok/s decode · 2.8s TTFT (warm) · 175 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	70.0 tok/s	1509 ms	40K
Coding	C	Tight fit	70.0 tok/s	2766 ms	40K
Agentic Coding	C	Runs with offload	70.0 tok/s	4024 ms	40K
Reasoning	C	Tight fit	70.0 tok/s	3269 ms	40K
RAG	C	Runs with offload	70.0 tok/s	5030 ms	40K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
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

Upgrade options

Hardware that runs Mamba Codestral 7B v0.1 well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.64 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 63%.114 tok/s decode

Raises estimated decode speed by about 63%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 45%.101.8 tok/s decode

Raises estimated decode speed by about 45%.

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can RTX 2060 Super 8GB run Mamba Codestral 7B v0.1?

Yes, RTX 2060 Super 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 70.0 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 6.8 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 2060 Super 8GB?

On RTX 2060 Super 8GB, Mamba Codestral 7B v0.1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 2060 Super 8GB receives a C grade with 70.0 tok/s and 40K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Mamba Codestral 7B v0.1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2060 Super 8GB See all hardware for Mamba Codestral 7B v0.1

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