Can Codestral Mamba 7B run on RTX 3060 Ti 8GB?

YES — Runs Great

A82Great

Estimated from fit model

Codestral Mamba 7B needs ~6.5 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~82 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 6.5 GB, 82.0 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

82.0 tok/s

TTFT

2360 ms

Safe context

67K

Memory

6.5 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on RTX 3060 Ti 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: 82.0 tok/s decode · 2.4s TTFT (warm) · 205 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	A	Runs well	82.0 tok/s	1287 ms	67K
Coding	A	Runs well	82.0 tok/s	2360 ms	67K
Agentic Coding	A	Tight fit	82.0 tok/s	3432 ms	67K
Reasoning	A	Runs well	82.0 tok/s	2789 ms	67K
RAG	A	Tight fit	82.0 tok/s	4290 ms	67K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A78
Q3_K_S	3	3.4 GB	Low	A79
NVFP4	4	3.9 GB	Medium	A78
Q4_K_M	4	4.3 GB	Medium	A78
Q5_K_MBest for your GPU	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
  --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your RTX 3060 Ti 8GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	A	27.4 tok/s
Qwen 3 8B	8B	A	29.9 tok/s
Nemotron Nano 8B	8B	A	36.9 tok/s
InternVL2 8B	8B	A	36.9 tok/s
Ministral 3 8B	8B	A	34.8 tok/s

Frequently asked questions

Can RTX 3060 Ti 8GB run Codestral Mamba 7B?

Yes, RTX 3060 Ti 8GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 82.0 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 6.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral Mamba 7B?

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

What speed will Codestral Mamba 7B run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, Codestral Mamba 7B achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2360ms using Q4_K_M quantization.

Can RTX 3060 Ti 8GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on RTX 3060 Ti 8GB receives a A grade with 82.0 tok/s and 67K context.

What context window can Codestral Mamba 7B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, Codestral Mamba 7B can safely use up to 67K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 3060 Ti 8GB See all hardware for Codestral Mamba 7B

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