Can Granite Code 20B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A81Great

Estimated from fit model

Granite Code 20B needs ~19.5 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 33.4 tok/s, Runs well

19.5 GB required32.0 GB available

61% VRAM used

Fit status

Runs well

Decode

33.4 tok/s

TTFT

5792 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 Radeon AI PRO R9700 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: 33.4 tok/s decode · 5.8s TTFT (warm) · 84 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	33.4 tok/s	3159 ms	8K
Coding	A	Runs well	33.4 tok/s	5792 ms	8K
Agentic Coding	A	Runs well	33.4 tok/s	8424 ms	8K
Reasoning	A	Runs well	33.4 tok/s	6845 ms	8K
RAG	A	Runs well	33.4 tok/s	10530 ms	8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on Radeon AI PRO R9700 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

Get started

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

Run

ollama run granite-code:20b

Your hardware

More models your Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.5 27B	27B	S	24.8 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Granite Code 20B?

Yes, Radeon AI PRO R9700 32GB can run Granite Code 20B with a A grade (Runs well). Expected decode speed: 33.4 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 19.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Granite Code 20B achieves approximately 33.4 tokens per second decode speed with a time-to-first-token of 5792ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on Radeon AI PRO R9700 32GB receives a A grade with 33.4 tok/s and 8K context.

What context window can Granite Code 20B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Granite Code 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Granite Code 20B

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