Can Granite 4.1 30B run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

A82Great

Estimated from fit model

Granite 4.1 30B needs ~26.3 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 26.3 GB, 22.2 tok/s, Tight fit

26.3 GB required32.0 GB available

82% VRAM used

Fit status

Tight fit

Decode

22.2 tok/s

TTFT

8728 ms

Safe context

39K

Memory

26.3 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B 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: 22.2 tok/s decode · 8.7s TTFT (warm) · 56 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	22.2 tok/s	4761 ms	39K
Coding	A	Tight fit	22.2 tok/s	8728 ms	39K
Agentic Coding	A	Tight fit	22.2 tok/s	12695 ms	39K
Reasoning	A	Tight fit	22.2 tok/s	10315 ms	39K
RAG	A	Tight fit	22.2 tok/s	15869 ms	39K

Inference speed

Granite 4.1 30B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite 4.1 30B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~71 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	70.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	32.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	29.0	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	27.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	26.8	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	25.8	Fits
RTX 3090 24GB	24 GB	Q4_K_M	24.8	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	10.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.7	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A79
Q3_K_S	3	14.7 GB	Low	A81
NVFP4	4	16.8 GB	Medium	A82
Q4_K_M	4	18.3 GB	Medium	A82
Q5_K_M	5	21.6 GB	High	A81
Q6_KBest for your GPU	6	24.6 GB	High	A81
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 4.1 30B on your machine.

Run

ollama run granite4.1:30b

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 3.6 35B A3B	35B	S	48 tok/s
Qwen 3.5 35B A3B	35B	S	52.2 tok/s
Qwen 3 32B	32B	S	21 tok/s
Qwen 3 30B A3B	30.5B	S	57.1 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Granite 4.1 30B?

Yes, Radeon AI PRO R9700 32GB can run Granite 4.1 30B with a A grade (Tight fit). Expected decode speed: 22.2 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 30B?

The recommended quantization for Granite 4.1 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 30B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Granite 4.1 30B achieves approximately 22.2 tokens per second decode speed with a time-to-first-token of 8728ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on Radeon AI PRO R9700 32GB receives a A grade with 22.2 tok/s and 39K context.

What context window can Granite 4.1 30B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Granite 4.1 30B can safely use up to 39K 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 30B

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