Can Granite 4.1 30B run on Radeon RX 7900M 16GB?

Q: What should I upgrade first if Granite 4.1 30B feels slow on Radeon RX 7900M 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

A70Great

Estimated from fit model

Granite 4.1 30B needs ~18.1 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q2_K quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

Granite 4.1 30B at Q4_K_M needs 24.7 GB — too much for Radeon RX 7900M 16GB (16.0 GB). Runs at Q2_K (18.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.7 GB, exceeds 16.0 GB available

24.7 GB required16.0 GB available

154% VRAM needed

8.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.0 tok/s

TTFT

32270 ms

Safe context

Memory

24.7 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite 4.1 30B on Radeon RX 7900M 16GB
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: 6.0 tok/s decode · 32.3s TTFT (warm) · 15 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.1 tok/s	14800 ms	4K
Coding	F	Too heavy	6.0 tok/s	32270 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	63932 ms	4K
Reasoning	F	Too heavy	6.0 tok/s	38137 ms	4K
RAG	F	Too heavy	4.4 tok/s	79915 ms	4K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4

Get started

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

Run

ollama run granite4.1:30b

Upgrade options

Hardware that runs Granite 4.1 30B well

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.26.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 347%.

~$999 MSRP

Radeon AI PRO R9700 32GBBest value

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.22.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.16.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,249 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+24)1555 GB/s (+979)

Makes the model fit on the accelerator instead of staying completely out of reach.76.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$10,000 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run Granite 4.1 30B?

Yes, Radeon RX 7900M 16GB can run Granite 4.1 30B at Q2_K quantization (Very compromised (needs ~1.4 GB host RAM)). The recommended Q4_K_M requires 24.7 GB which exceeds available memory, but at Q2_K it needs only 18.1 GB. Expected decode speed: 15.3 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 24.7 GB at Q4_K_M quantization. On Radeon RX 7900M 16GB, it fits at Q2_K using 18.1 GB.

What is the best quantization for Granite 4.1 30B?

The recommended quantization is Q4_K_M, but on Radeon RX 7900M 16GB the best fitting quantization is Q2_K, which uses 18.1 GB.

What speed will Granite 4.1 30B run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Granite 4.1 30B achieves approximately 15.3 tokens per second decode speed with a time-to-first-token of 12618ms using Q2_K quantization.

Can Radeon RX 7900M 16GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on Radeon RX 7900M 16GB receives a F grade with 6.0 tok/s and 4K context.

What context window can Granite 4.1 30B use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Granite 4.1 30B can safely use up to 7K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for Granite 4.1 30B

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