Can Granite 4.1 8B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A73Great

Estimated from fit model

Granite 4.1 8B needs ~11.4 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~77 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) — 11.4 GB, 83.2 tok/s, Runs well

11.4 GB required32.0 GB available

36% VRAM used

Fit status

Runs well

Decode

83.2 tok/s

TTFT

2327 ms

Safe context

131K

Memory

11.4 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B 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: 83.2 tok/s decode · 2.3s TTFT (warm) · 208 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	83.2 tok/s	1270 ms	131K
Coding	A	Runs well	77.4 tok/s	2502 ms	131K
Agentic Coding	A	Runs well	83.2 tok/s	3385 ms	131K
Reasoning	A	Runs well	83.2 tok/s	2751 ms	131K
RAG	A	Runs well	83.2 tok/s	4232 ms	131K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B67
Q3_K_S	3	3.9 GB	Low	B68
NVFP4	4	4.5 GB	Medium	B68
Q4_K_M	4	4.9 GB	Medium	B68
Q5_K_M	5	5.8 GB	High	B68
Q6_K	6	6.6 GB	High	B69
Q8_0	8	8.6 GB	Very High	B69
F16Best for your GPU	16	16.4 GB	Maximum	A73

Get started

Copy-paste commands to run Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Your hardware

More models your Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.5 27B	27B	S	24.8 tok/s
Qwen 3.6 27B	27B	S	18.8 tok/s
Qwen 3.6 35B A3B	35B	S	48 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	59.1 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Granite 4.1 8B?

Yes, Radeon AI PRO R9700 32GB can run Granite 4.1 8B with a A grade (Runs well). Expected decode speed: 77.4 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 8B?

The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 8B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Granite 4.1 8B achieves approximately 77.4 tokens per second decode speed with a time-to-first-token of 2502ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on Radeon AI PRO R9700 32GB receives a A grade with 77.4 tok/s and 131K context.

What context window can Granite 4.1 8B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Granite 4.1 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Granite 4.1 8B

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