Can Codestral Mamba 7B run on Radeon Pro W7800 32GB?

YES — Runs Great

A73Great

Estimated from fit model

Codestral Mamba 7B needs ~8.9 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~92 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.9 GB, 91.5 tok/s, Runs well

8.9 GB required32.0 GB available

28% VRAM used

Fit status

Runs well

Decode

91.5 tok/s

TTFT

2115 ms

Safe context

262K

Memory

8.9 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on Radeon Pro W7800 32GB
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: 91.5 tok/s decode · 2.1s TTFT (warm) · 229 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	91.5 tok/s	1154 ms	262K
Coding	A	Runs well	91.5 tok/s	2115 ms	262K
Agentic Coding	A	Runs well	91.5 tok/s	3077 ms	262K
Reasoning	A	Runs well	91.5 tok/s	2500 ms	262K
RAG	A	Runs well	91.5 tok/s	3846 ms	262K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B68
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B69
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0	8	7.5 GB	Very High	B70
F16Best for your GPU	16	14.3 GB	Maximum	A73

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 Radeon Pro W7800 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.4 tok/s
Qwen 3.5 27B	27B	S	22.3 tok/s
Qwen 3.6 27B	27B	S	16.9 tok/s
Qwen 3.6 35B A3B	35B	S	43.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.1 tok/s

Frequently asked questions

Can Radeon Pro W7800 32GB run Codestral Mamba 7B?

Yes, Radeon Pro W7800 32GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 91.5 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 8.9 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 Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral Mamba 7B achieves approximately 91.5 tokens per second decode speed with a time-to-first-token of 2115ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on Radeon Pro W7800 32GB receives a A grade with 91.5 tok/s and 262K context.

What context window can Codestral Mamba 7B use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, 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 Radeon Pro W7800 32GB See all hardware for Codestral Mamba 7B

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