Can Codestral Mamba 7B run on Tesla P40 24GB?

YES — Runs Great

A73Great

○Estimated from fit model

Codestral Mamba 7B needs ~8.1 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~55 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) — 8.1 GB, 55.0 tok/s, Runs well

8.1 GB required24.0 GB available

34% VRAM used

Fit status

Runs well

Decode

55.0 tok/s

TTFT

3521 ms

Safe context

262K

Memory

8.1 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on Tesla P40 24GB
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: 55.0 tok/s decode · 3.5s TTFT (warm) · 137 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	A	Runs well	55.0 tok/s	1921 ms	262K
Coding	A	Runs well	55.0 tok/s	3521 ms	262K
Agentic Coding	A	Runs well	55.0 tok/s	5122 ms	262K
Reasoning	A	Runs well	55.0 tok/s	4162 ms	262K
RAG	A	Runs well	55.0 tok/s	6402 ms	262K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B69
Q3_K_S	3	3.4 GB	Low	B70
NVFP4	4	3.9 GB	Medium	A70
Q4_K_M	4	4.3 GB	Medium	A70
Q5_K_M	5	5.0 GB	High	A71
Q6_K	6	5.7 GB	High	A71
Q8_0	8	7.5 GB	Very High	A72
F16Best for your GPU	16	14.3 GB	Maximum	A75

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 Tesla P40 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	13.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	10.2 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	A	12.7 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	31.9 tok/s

Frequently asked questions

See all results for Tesla P40 24GB See all hardware for Codestral Mamba 7B

URL: https://willitrunai.com/can-run/codestral-mamba-7b-on-tesla-p40-24gb

⇱ Codestral Mamba 7B on Tesla P40 24GB? YES