Can Mamba Codestral 7B v0.1 run on RTX 4000 Ada 20GB?

YES — Runs Great

C49Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~8.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~76 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) — 8.0 GB, 75.6 tok/s, Runs well

8.0 GB required20.0 GB available

40% VRAM used

Fit status

Runs well

Decode

75.6 tok/s

TTFT

2560 ms

Safe context

250K

Memory

8.0 GB / 20.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on RTX 4000 Ada 20GB
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: 75.6 tok/s decode · 2.6s TTFT (warm) · 189 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	C	Runs well	75.6 tok/s	1396 ms	250K
Coding	C	Runs well	75.6 tok/s	2560 ms	250K
Agentic Coding	C	Runs well	75.6 tok/s	3724 ms	250K
Reasoning	C	Runs well	75.6 tok/s	3025 ms	250K
RAG	C	Runs well	75.6 tok/s	4655 ms	250K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C45
Q3_K_S	3	3.4 GB	Low	C45
NVFP4	4	3.9 GB	Medium	C46
Q4_K_M	4	4.3 GB	Medium	C46
Q5_K_M	5	5.0 GB	High	C46
Q6_K	6	5.7 GB	High	C47
Q8_0	8	7.5 GB	Very High	C48
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

Frequently asked questions

Can RTX 4000 Ada 20GB run Mamba Codestral 7B v0.1?

Yes, RTX 4000 Ada 20GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 75.6 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 8.0 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Mamba Codestral 7B v0.1 achieves approximately 75.6 tokens per second decode speed with a time-to-first-token of 2560ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 4000 Ada 20GB receives a C grade with 75.6 tok/s and 250K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Mamba Codestral 7B v0.1 can safely use up to 250K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Mamba Codestral 7B v0.1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

75.6 tok/s

1396 ms

250K

Coding

Runs well

75.6 tok/s

2560 ms

250K

Agentic Coding

Runs well

75.6 tok/s

3724 ms

250K

Reasoning

Runs well

75.6 tok/s

3025 ms

250K

RAG

Runs well

75.6 tok/s

4655 ms

250K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C45
Q3_K_S	3	3.4 GB	Low	C45
NVFP4	4	3.9 GB	Medium	C46
Q4_K_M	4	4.3 GB	Medium	C46
Q5_K_M	5	5.0 GB	High	C46
Q6_K	6	5.7 GB	High	C47
Q8_0	8	7.5 GB	Very High	C48
F16Best for your GPU	16	14.3 GB	Maximum	C50

Frequently asked questions

Can RTX 4000 Ada 20GB run Mamba Codestral 7B v0.1?

Yes, RTX 4000 Ada 20GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 75.6 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 8.0 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Mamba Codestral 7B v0.1 achieves approximately 75.6 tokens per second decode speed with a time-to-first-token of 2560ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 4000 Ada 20GB receives a C grade with 75.6 tok/s and 250K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Mamba Codestral 7B v0.1 can safely use up to 250K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.