Can GPT-OSS 20B run on Radeon Pro W7500 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

GPT-OSS 20B needs ~17.0 GB but Radeon Pro W7500 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very 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) — 17.0 GB, exceeds 8.0 GB available

17.0 GB required8.0 GB available

213% VRAM needed

9.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

49429 ms

Safe context

Memory

17.0 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 20B on Radeon Pro W7500 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: 3.9 tok/s decode · 49.4s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 17.0 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	4.6 tok/s	23037 ms	4K
Coding	F	Too heavy	3.9 tok/s	49429 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	73996 ms	4K
Reasoning	F	Too heavy	3.9 tok/s	58416 ms	4K
RAG	F	Too heavy	3.8 tok/s	92494 ms	4K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	F0
Q3_K_S	3	10.3 GB	Low	F0
NVFP4	4	11.8 GB	Medium	F0
Q4_K_M	4	12.8 GB	Medium	F0
Q5_K_M	5	15.1 GB	High	F0
Q6_K	6	17.2 GB	High	F0
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien GPT-OSS 20B

RX 7600 XT 16GBMejor relación calidad-precio

16 GB VRAM (+8)288 GB/s (+64)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.19.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 395%.

~$329 MSRP

RX 9060 XT 16GBMejora AMD

16 GB VRAM (+8)320 GB/s (+96)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.23.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 497%.

~$349 MSRP

RX 7900 XT 20GBOpción económica

20 GB VRAM (+12)800 GB/s (+576)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.92.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$899 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run GPT-OSS 20B?

No, GPT-OSS 20B requires more memory than Radeon Pro W7500 8GB provides.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.0 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 20B?

The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 20B run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, GPT-OSS 20B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49429ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on Radeon Pro W7500 8GB receives a F grade with 3.9 tok/s and 4K context.

What context window can GPT-OSS 20B use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, GPT-OSS 20B can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 20B feels slow on Radeon Pro W7500 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.

See all results for Radeon Pro W7500 8GB See all hardware for GPT-OSS 20B

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