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

YES — Runs Great

A74Great

Estimated from fit model

Granite Code 8B needs ~10.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~83 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) — 10.9 GB, 83.2 tok/s, Runs well

10.9 GB required32.0 GB available

34% VRAM used

Fit status

Runs well

Decode

83.2 tok/s

TTFT

2327 ms

Safe context

Memory

10.9 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite Code 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	8K
Coding	A	Runs well	83.2 tok/s	2327 ms	8K
Agentic Coding	A	Runs well	83.2 tok/s	3385 ms	8K
Reasoning	A	Runs well	83.2 tok/s	2751 ms	8K
RAG	A	Runs well	83.2 tok/s	4232 ms	8K

Quantization options

How Granite Code 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	B68
Q3_K_S	3	3.9 GB	Low	B68
NVFP4	4	4.5 GB	Medium	B69
Q4_K_M	4	4.9 GB	Medium	B69
Q5_K_M	5	5.8 GB	High	B69
Q6_K	6	6.6 GB	High	B69
Q8_0	8	8.6 GB	Very High	A70
F16Best for your GPU	16	16.4 GB	Maximum	A74

Get started

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

Run

ollama run granite-code: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 Code 8B?

Yes, Radeon AI PRO R9700 32GB can run Granite Code 8B with a A grade (Runs well). Expected decode speed: 83.2 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

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

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

On Radeon AI PRO R9700 32GB, Granite Code 8B achieves approximately 83.2 tokens per second decode speed with a time-to-first-token of 2327ms using Q4_K_M quantization.

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

For coding workloads, Granite Code 8B on Radeon AI PRO R9700 32GB receives a A grade with 83.2 tok/s and 8K context.

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

On Radeon AI PRO R9700 32GB, Granite Code 8B 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 8B

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