Can Mamba Codestral 7B v0.1 run on Tesla P40 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~8.4 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) — 8.4 GB, 55.0 tok/s, Runs well

8.4 GB required24.0 GB available

35% VRAM used

Fit status

Runs well

Decode

55.0 tok/s

TTFT

3521 ms

Safe context

320K

Memory

8.4 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 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	C	Runs well	55.0 tok/s	1921 ms	320K
Coding	C	Runs well	55.0 tok/s	3521 ms	320K
Agentic Coding	C	Runs well	55.0 tok/s	5122 ms	320K
Reasoning	C	Runs well	55.0 tok/s	4162 ms	320K
RAG	C	Runs well	55.0 tok/s	6402 ms	320K

Quantization options

How Mamba Codestral 7B v0.1 (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	C44
Q3_K_S	3	3.4 GB	Low	C44
NVFP4	4	3.9 GB	Medium	C45
Q4_K_M	4	4.3 GB	Medium	C45
Q5_K_M	5	5.0 GB	High	C45
Q6_K	6	5.7 GB	High	C45
Q8_0	8	7.5 GB	Very High	C47
F16Best for your GPU	16	14.3 GB	Maximum	C50

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

Opções de upgrade

Hardware que roda bem Mamba Codestral 7B v0.1

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 142%.133 tok/s decodificação

Raises estimated decode speed by about 142%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 78%.98 tok/s decodificação

Raises estimated decode speed by about 78%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 78%.98 tok/s decodificação

Raises estimated decode speed by about 78%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Mamba Codestral 7B v0.1?

Yes, Tesla P40 24GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 55.0 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 8.4 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 Tesla P40 24GB?

On Tesla P40 24GB, Mamba Codestral 7B v0.1 achieves approximately 55.0 tokens per second decode speed with a time-to-first-token of 3521ms using Q4_K_M quantization.

Can Tesla P40 24GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on Tesla P40 24GB receives a C grade with 55.0 tok/s and 320K context.

What context window can Mamba Codestral 7B v0.1 use on Tesla P40 24GB?

On Tesla P40 24GB, Mamba Codestral 7B v0.1 can safely use up to 320K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Mamba Codestral 7B v0.1

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