Can Codestral Mamba 7B run on H100 NVL 188GB?

YES — Runs Great

B70Good

Estimated from fit model

Codestral Mamba 7B needs ~24.8 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 24.8 GB, 98.0 tok/s, Runs well

24.8 GB required188.0 GB available

13% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

262K

Memory

24.8 GB / 188.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on H100 NVL 188GB
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	B	Runs well	98.0 tok/s	1078 ms	262K
Coding	B	Runs well	98.0 tok/s	1976 ms	262K
Agentic Coding	B	Runs well	98.0 tok/s	2873 ms	262K
Reasoning	B	Runs well	98.0 tok/s	2335 ms	262K
RAG	B	Runs well	98.0 tok/s	3592 ms	262K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B62
Q3_K_S	3	3.4 GB	Low	B62
NVFP4	4

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

Frequently asked questions

Can H100 NVL 188GB run Codestral Mamba 7B?

Yes, H100 NVL 188GB can run Codestral Mamba 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 24.8 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 H100 NVL 188GB?

On H100 NVL 188GB, Codestral Mamba 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can H100 NVL 188GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on H100 NVL 188GB receives a B grade with 98.0 tok/s and 262K context.

What context window can Codestral Mamba 7B use on H100 NVL 188GB?

On H100 NVL 188GB, Codestral Mamba 7B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for Codestral Mamba 7B

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