Can Granite 4.1 30B run on RX 9070 16GB?

YES — With Q2_K

A71Great

Estimated from fit model

Granite 4.1 30B needs ~18.1 GB VRAM. RX 9070 16GB has 16.0 GB. With Q2_K quantization, expect ~18 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 RX 9070 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

7.3 tok/s

TTFT

26351 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 RX 9070 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: 7.3 tok/s decode · 26.4s TTFT (warm) · 18 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	8.7 tok/s	12151 ms	4K
Coding	F	Too heavy	6.8 tok/s	28328 ms	4K
Agentic Coding	F	Too heavy	5.1 tok/s	55589 ms	4K
Reasoning	F	Too heavy	7.3 tok/s	31143 ms	4K
RAG	F	Too heavy	5.4 tok/s	64638 ms	4K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on RX 9070 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	16.8 GB	Medium	F0
Q4_K_M	4	18.3 GB	Medium	F0
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
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

升级选项

能流畅运行 Granite 4.1 30B 的硬件

RX 7900 XTX 24GB经济之选

24 GB VRAM (+8)960 GB/s (+320)

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

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

Raises estimated decode speed by about 267%.

~$999 MSRP

Radeon AI PRO R9700 32GB最佳性价比

32 GB VRAM (+16)

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

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 升级

32 GB VRAM (+16)

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

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 40GB最大飞跃

40 GB VRAM (+24)1555 GB/s (+915)

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

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 RX 9070 16GB run Granite 4.1 30B?

Yes, RX 9070 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: 18.4 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 RX 9070 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 RX 9070 16GB the best fitting quantization is Q2_K, which uses 18.1 GB.

What speed will Granite 4.1 30B run at on RX 9070 16GB?

On RX 9070 16GB, Granite 4.1 30B achieves approximately 18.4 tokens per second decode speed with a time-to-first-token of 10514ms using Q2_K quantization.

Can RX 9070 16GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on RX 9070 16GB receives a F grade with 6.8 tok/s and 4K context.

What context window can Granite 4.1 30B use on RX 9070 16GB?

On RX 9070 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.

What should I upgrade first if Granite 4.1 30B feels slow on RX 9070 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.

See all results for RX 9070 16GB See all hardware for Granite 4.1 30B

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