Can Granite Code 20B run on AMD Instinct MI100 32GB?

YES — Runs Great

A84Great

○Estimated from fit model

Granite Code 20B needs ~19.5 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~71 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) — 19.5 GB, 70.7 tok/s, Runs well

19.5 GB required32.0 GB available

61% VRAM used

Fit status

Runs well

Decode

70.7 tok/s

TTFT

2740 ms

Safe context

Memory

19.5 GB / 32.0 GB

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite Code 20B 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: 70.7 tok/s decode · 2.7s TTFT (warm) · 177 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	A	Runs well	70.7 tok/s	1494 ms	8K
Coding	A	Runs well	70.7 tok/s	2740 ms	8K
Agentic Coding	S	Runs well	70.7 tok/s	3985 ms	8K
Reasoning	A	Runs well	70.7 tok/s	3238 ms	8K
RAG	S	Runs well	70.7 tok/s	4982 ms	8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A75
Q3_K_S	3	9.8 GB	Low	A75
NVFP4	4	11.2 GB	Medium	A76
Q4_K_M	4	12.2 GB	Medium	A77
Q5_K_M	5	14.4 GB	High	A78
Q6_K	6	16.4 GB	High	A79
Q8_0Best for your GPU	8	21.4 GB	Very High	A79
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Your hardware

More models your AMD Instinct MI100 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	120.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	52.3 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	32.6 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	101.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	124.8 tok/s

Frequently asked questions

See all results for AMD Instinct MI100 32GB See all hardware for Granite Code 20B

URL: https://willitrunai.com/can-run/granite-code-20b-on-instinct-mi100-32gb

⇱ Granite Code 20B on AMD Instinct MI100 32GB? YES