Can Mamba Codestral 7B v0.1 run on RX 7600 XT 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~7.6 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~45 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) — 7.6 GB, 45.0 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

45.0 tok/s

TTFT

4303 ms

Safe context

180K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on RX 7600 XT 16GB
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: 45.0 tok/s decode · 4.3s TTFT (warm) · 113 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	45.0 tok/s	2347 ms	180K
Coding	C	Runs well	45.0 tok/s	4303 ms	180K
Agentic Coding	C	Runs well	45.0 tok/s	6260 ms	180K
Reasoning	C	Runs well	45.0 tok/s	5086 ms	180K
RAG	C	Runs well	45.0 tok/s	7824 ms	180K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
F16	16	14.3 GB	Maximum	F0

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

Opciones de mejora

Hardware que ejecuta bien Mamba Codestral 7B v0.1

RX 7900 XT 20GBOpción económica

20 GB VRAM (+4)800 GB/s (+512)

Sube la velocidad estimada de decodificación alrededor de un 118%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 118%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RTX 4000 Ada 20GBMejor relación calidad-precio

20 GB VRAM (+4)360 GB/s (+72)

Sube la velocidad estimada de decodificación alrededor de un 68%.75.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 68%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,250 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run Mamba Codestral 7B v0.1?

Yes, RX 7600 XT 16GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 45.0 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 7.6 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 RX 7600 XT 16GB?

On RX 7600 XT 16GB, Mamba Codestral 7B v0.1 achieves approximately 45.0 tokens per second decode speed with a time-to-first-token of 4303ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RX 7600 XT 16GB receives a C grade with 45.0 tok/s and 180K context.

What context window can Mamba Codestral 7B v0.1 use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Mamba Codestral 7B v0.1 can safely use up to 180K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for Mamba Codestral 7B v0.1

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