Can Codestral Mamba 7B run on RTX 3080 10GB?

YES — Runs Great

A82Great

○Estimated from fit model

Codestral Mamba 7B needs ~7.0 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~98 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) — 7.0 GB, 98.0 tok/s, Runs well

7.0 GB required10.0 GB available

70% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

116K

Memory

7.0 GB / 10.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on RTX 3080 10GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	116K
Coding	A	Runs well	98.0 tok/s	1976 ms	116K
Agentic Coding	A	Runs well	98.0 tok/s	2873 ms	116K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	116K
RAG	A	Runs well	98.0 tok/s	3592 ms	116K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A76
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4	3.9 GB	Medium	A78
Q4_K_M	4	4.3 GB	Medium	A78
Q5_K_M	5	5.0 GB	High	A78
Q6_KBest for your GPU	6	5.7 GB	High	A78
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 3080 10GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	113.1 tok/s
👁 Alibaba Qwen 3 8B	8B	S	112 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	S	112 tok/s
👁 InternLM InternVL2 8B	8B	S	112 tok/s
👁 Mistral Ministral 3 8B	8B	A	112 tok/s

Frequently asked questions

See all results for RTX 3080 10GB See all hardware for Codestral Mamba 7B

URL: https://willitrunai.com/can-run/codestral-mamba-7b-on-rtx-3080-10gb

⇱ Codestral Mamba 7B on RTX 3080 10GB? YES