Can Codestral Mamba 7B run on RX 7700 XT 12GB?

YES — Runs Great

A78Great

Estimated from fit model

Codestral Mamba 7B needs ~6.9 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~70 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) — 6.9 GB, 69.8 tok/s, Runs well

6.9 GB required12.0 GB available

58% VRAM used

Fit status

Runs well

Decode

69.8 tok/s

TTFT

2773 ms

Safe context

184K

Memory

6.9 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on RX 7700 XT 12GB
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: 69.8 tok/s decode · 2.8s TTFT (warm) · 175 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	69.8 tok/s	1513 ms	184K
Coding	A	Runs well	69.8 tok/s	2773 ms	184K
Agentic Coding	A	Runs well	69.8 tok/s	4034 ms	184K
Reasoning	A	Runs well	69.8 tok/s	3278 ms	184K
RAG	A	Runs well	69.8 tok/s	5042 ms	184K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A75
NVFP4	4	3.9 GB	Medium	A76
Q4_K_M	4	4.3 GB	Medium	A76
Q5_K_M	5	5.0 GB	High	A77
Q6_K	6	5.7 GB	High	A78
Q8_0Best for your GPU	8	7.5 GB	Very High	A77
F16	16	14.3 GB	Maximum	F0

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 RX 7700 XT 12GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	50.8 tok/s
Qwen 3 14B	14B	A	20.5 tok/s
Qwen 3 8B	8B	S	57.1 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	16.6 tok/s
Nemotron Nano 8B	8B	S	57.1 tok/s

Frequently asked questions

Can RX 7700 XT 12GB run Codestral Mamba 7B?

Yes, RX 7700 XT 12GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 69.8 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 6.9 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 RX 7700 XT 12GB?

On RX 7700 XT 12GB, Codestral Mamba 7B achieves approximately 69.8 tokens per second decode speed with a time-to-first-token of 2773ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on RX 7700 XT 12GB receives a A grade with 69.8 tok/s and 184K context.

What context window can Codestral Mamba 7B use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Codestral Mamba 7B can safely use up to 184K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RX 7700 XT 12GB See all hardware for Codestral Mamba 7B

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