Can Codestral Mamba 7B run on Radeon PRO W7600 8GB?

YES — Runs Great

A80Great

Estimated from fit model

Codestral Mamba 7B needs ~6.5 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~46 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) — 6.5 GB, 45.8 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

45.8 tok/s

TTFT

4230 ms

Safe context

67K

Memory

6.5 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on Radeon PRO W7600 8GB
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: 45.8 tok/s decode · 4.2s TTFT (warm) · 114 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	45.8 tok/s	2308 ms	67K
Coding	A	Runs well	45.8 tok/s	4230 ms	67K
Agentic Coding	A	Tight fit	45.8 tok/s	6153 ms	67K
Reasoning	A	Runs well	45.8 tok/s	5000 ms	67K
RAG	A	Tight fit	45.8 tok/s	7692 ms	67K

Inference speed

Codestral Mamba 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral Mamba 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A78
Q3_K_S	3	3.4 GB	Low	A79
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

Your hardware

More models your Radeon PRO W7600 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	17.8 tok/s
Qwen 3 8B	8B	A	23.1 tok/s

Frequently asked questions

Can Radeon PRO W7600 8GB run Codestral Mamba 7B?

Yes, Radeon PRO W7600 8GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 45.8 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 6.5 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 W7600 8GB?

On Radeon PRO W7600 8GB, Codestral Mamba 7B achieves approximately 45.8 tokens per second decode speed with a time-to-first-token of 4230ms using Q4_K_M quantization.

Can Radeon PRO W7600 8GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on Radeon PRO W7600 8GB receives a A grade with 45.8 tok/s and 67K context.

What context window can Codestral Mamba 7B use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, Codestral Mamba 7B can safely use up to 67K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for Radeon PRO W7600 8GB See all hardware for Codestral Mamba 7B

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