Can Devstral 2 123B Instruct run on RX 9060 8GB?

Q: What should I upgrade first if Devstral 2 123B Instruct feels slow on RX 9060 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Devstral 2 123B Instruct needs ~82.1 GB but RX 9060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 82.1 GB, exceeds 8.0 GB available

82.1 GB required8.0 GB available

1026% VRAM needed

74.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

82.1 GB / 8.0 GB

Offload

90%

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDevstral 2 123B Instruct on RX 9060 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 82.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	F0
Q3_K_S	3	60.3 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Devstral 2 123B Instruct well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+280)8000 GB/s (+7712)

Makes the model fit on the accelerator instead of staying completely out of reach.84.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

AMD Instinct MI300A 128GBBest value

128 GB VRAM (+120)5300 GB/s (+5012)

Makes the model fit on the accelerator instead of staying completely out of reach.53.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

AMD Instinct MI250X 128GBAMD upgrade

128 GB VRAM (+120)3200 GB/s (+2912)

Makes the model fit on the accelerator instead of staying completely out of reach.36.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

Can RX 9060 8GB run Devstral 2 123B Instruct?

No, Devstral 2 123B Instruct requires more memory than RX 9060 8GB provides.

How much VRAM does Devstral 2 123B Instruct need?

Devstral 2 123B Instruct (123B parameters) requires approximately 82.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral 2 123B Instruct?

The recommended quantization for Devstral 2 123B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral 2 123B Instruct run at on RX 9060 8GB?

On RX 9060 8GB, Devstral 2 123B Instruct achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RX 9060 8GB run Devstral 2 123B Instruct for coding?

For coding workloads, Devstral 2 123B Instruct on RX 9060 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Devstral 2 123B Instruct use on RX 9060 8GB?

On RX 9060 8GB, Devstral 2 123B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral 2 123B Instruct feels slow on RX 9060 8GB?

See all results for RX 9060 8GB See all hardware for Devstral 2 123B Instruct

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