Can Qwen 2.5 32B run on AMD Instinct MI100 32GB?

YES — Tight Fit

S85Excellent

○Estimated from fit model

Qwen 2.5 32B needs ~27.5 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 27.5 GB, 44.2 tok/s, Tight fit

27.5 GB required32.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

44.2 tok/s

TTFT

4384 ms

Safe context

34K

Memory

27.5 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B on AMD Instinct MI100 32GB
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: 44.2 tok/s decode · 4.4s TTFT (warm) · 110 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	44.2 tok/s	2391 ms	34K
Coding	S	Tight fit	44.2 tok/s	4384 ms	34K
Agentic Coding	S	Runs with offload	44.2 tok/s	6376 ms	34K
Reasoning	S	Tight fit	44.2 tok/s	5181 ms	34K
RAG	S	Runs with offload	44.2 tok/s	7971 ms	34K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A81
Q3_K_S	3	15.7 GB	Low	A83
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 32B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your AMD Instinct MI100 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	101.4 tok/s
👁 Alibaba

Frequently asked questions

See all results for AMD Instinct MI100 32GB See all hardware for Qwen 2.5 32B

URL: https://willitrunai.com/can-run/qwen-2.5-32b-on-instinct-mi100-32gb

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