Can Granite 4.1 30B run on RTX A4500 20GB?

YES — With NVFP4

B65Good

Estimated from fit model

Granite 4.1 30B needs ~23.9 GB VRAM. RTX A4500 20GB has 20.0 GB. With NVFP4 quantization, expect ~17 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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 25.4 GB — too much for RTX A4500 20GB (20.0 GB). Runs at NVFP4 (23.9 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.4 GB, exceeds 20.0 GB available

25.4 GB required20.0 GB available

127% VRAM needed

5.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.3 tok/s

TTFT

14566 ms

Safe context

Memory

25.4 GB / 20.0 GB

Offload

20%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite 4.1 30B on RTX A4500 20GB
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: 13.3 tok/s decode · 14.6s TTFT (warm) · 33 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 20% 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 2.7 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	A	Very compromised (needs ~2.7 GB host RAM)	15.7 tok/s	6714 ms	4K
Coding	F	Too heavy	13.3 tok/s	14566 ms	4K
Agentic Coding	F	Too heavy	9.8 tok/s	28630 ms	4K
Reasoning	F	Too heavy	13.3 tok/s	17215 ms	4K
RAG	F	Too heavy	9.8 tok/s	35787 ms	4K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_SBest for your GPU	3	14.7 GB	Low	A82
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

Upgrade options

Hardware that runs Granite 4.1 30B well

RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+296)

Makes the model fit on the accelerator instead of staying completely out of reach.24.8 tok/s decode

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

Raises estimated decode speed by about 86%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+368)

Makes the model fit on the accelerator instead of staying completely out of reach.29 tok/s decode

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

Raises estimated decode speed by about 118%.

~$1,599 MSRP

RTX A5500 24GBNVIDIA upgrade

24 GB VRAM (+4)768 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.22.7 tok/s decode

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

Raises estimated decode speed by about 71%.

~$3,200 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+20)1555 GB/s (+915)

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 RTX A4500 20GB run Granite 4.1 30B?

Yes, RTX A4500 20GB can run Granite 4.1 30B at NVFP4 quantization (Very compromised (needs ~2.7 GB host RAM)). The recommended Q4_K_M requires 25.4 GB which exceeds available memory, but at NVFP4 it needs only 23.9 GB. Expected decode speed: 17.3 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.4 GB at Q4_K_M quantization. On RTX A4500 20GB, it fits at NVFP4 using 23.9 GB.

What is the best quantization for Granite 4.1 30B?

The recommended quantization is Q4_K_M, but on RTX A4500 20GB the best fitting quantization is NVFP4, which uses 23.9 GB.

What speed will Granite 4.1 30B run at on RTX A4500 20GB?

On RTX A4500 20GB, Granite 4.1 30B achieves approximately 17.3 tokens per second decode speed with a time-to-first-token of 11204ms using NVFP4 quantization.

Can RTX A4500 20GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on RTX A4500 20GB receives a F grade with 13.3 tok/s and 4K context.

What context window can Granite 4.1 30B use on RTX A4500 20GB?

On RTX A4500 20GB, Granite 4.1 30B can safely use up to 4K tokens of context at NVFP4 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 RTX A4500 20GB?

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 RTX A4500 20GB See all hardware for Granite 4.1 30B

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Preview:

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 20% 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 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Very compromised (needs ~2.7 GB host RAM)

15.7 tok/s

6714 ms

Coding

Too heavy

13.3 tok/s

14566 ms

Agentic Coding

Too heavy

9.8 tok/s

28630 ms

Reasoning

Too heavy

13.3 tok/s

17215 ms

RAG

Too heavy

9.8 tok/s

35787 ms

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_SBest for your GPU	3	14.7 GB	Low	A82
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

Frequently asked questions

Can RTX A4500 20GB run Granite 4.1 30B?

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.4 GB at Q4_K_M quantization. On RTX A4500 20GB, it fits at NVFP4 using 23.9 GB.

What is the best quantization for Granite 4.1 30B?

The recommended quantization is Q4_K_M, but on RTX A4500 20GB the best fitting quantization is NVFP4, which uses 23.9 GB.

What speed will Granite 4.1 30B run at on RTX A4500 20GB?

On RTX A4500 20GB, Granite 4.1 30B achieves approximately 17.3 tokens per second decode speed with a time-to-first-token of 11204ms using NVFP4 quantization.

Can RTX A4500 20GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on RTX A4500 20GB receives a F grade with 13.3 tok/s and 4K context.

What context window can Granite 4.1 30B use on RTX A4500 20GB?

On RTX A4500 20GB, Granite 4.1 30B can safely use up to 4K tokens of context at NVFP4 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 RTX A4500 20GB?

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.