Can GPT-OSS 120B run on AMD Instinct MI250 128GB?

YES — Runs Great

S94Excellent

○Estimated from fit model

GPT-OSS 120B needs ~90.0 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 90.0 GB, 33.2 tok/s, Runs well

90.0 GB required128.0 GB available

70% VRAM used

Fit status

Runs well

Decode

33.2 tok/s

TTFT

5839 ms

Safe context

131K

Memory

90.0 GB / 128.0 GB

Memory breakdown

Weights71.4 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsGPT-OSS 120B on AMD Instinct MI250 128GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 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	S	Runs well	33.2 tok/s	3185 ms	131K
Coding	S	Runs well	33.2 tok/s	5839 ms	131K
Agentic Coding	S	Runs well	33.2 tok/s	8493 ms	131K
Reasoning	S	Runs well	33.2 tok/s	6901 ms	131K
RAG	S	Runs well	33.2 tok/s	10616 ms	131K

Quantization options

How GPT-OSS 120B (117B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	45.6 GB	Low	A84
Q3_K_S	3	57.3 GB	Low	S86
NVFP4	4	65.5 GB	Medium	S88
Q4_K_M	4	71.4 GB	Medium	S88
Q5_K_M	5	84.2 GB	High	S88
Q6_KBest for your GPU	6	95.9 GB	High	S88
Q8_0	8	125.2 GB	Very High	F0
F16	16	239.8 GB	Maximum	F0

Get started

Copy-paste commands to run GPT-OSS 120B on your machine.

Run

ollama run gpt-oss:120b

Your hardware

More models your AMD Instinct MI250 128GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	31.5 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	87.5 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	94.8 tok/s

Frequently asked questions

See all results for AMD Instinct MI250 128GB See all hardware for GPT-OSS 120B

URL: https://willitrunai.com/can-run/gpt-oss-120b-on-instinct-mi250-128gb

⇱ GPT-OSS 120B on AMD Instinct MI250 128GB? YES