Can Granite 4.1 30B run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Granite 4.1 30B feels slow on RTX 4000 Ada 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.

YES — With NVFP4

B63Good

Estimated from fit model

Granite 4.1 30B needs ~23.9 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With NVFP4 quantization, expect ~10 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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 4000 Ada 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

7.5 tok/s

TTFT

25895 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 4000 Ada 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: 7.5 tok/s decode · 25.9s TTFT (warm) · 19 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	B	Very compromised (needs ~2.7 GB host RAM)	8.8 tok/s	11936 ms	4K
Coding	F	Too heavy	7.5 tok/s	25895 ms	4K
Agentic Coding	F	Too heavy	5.5 tok/s	50897 ms	4K
Reasoning	F	Too heavy	7.5 tok/s	30604 ms	4K
RAG	F	Too heavy	5.5 tok/s	63622 ms	4K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on RTX 4000 Ada 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

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 (+576)

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 231%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

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 287%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decode

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

Raises estimated decode speed by about 192%.

~$1,599 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+20)1555 GB/s (+1195)

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 4000 Ada 20GB run Granite 4.1 30B?

Yes, RTX 4000 Ada 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: 9.7 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 4000 Ada 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 4000 Ada 20GB the best fitting quantization is NVFP4, which uses 23.9 GB.

What speed will Granite 4.1 30B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Granite 4.1 30B achieves approximately 9.7 tokens per second decode speed with a time-to-first-token of 19919ms using NVFP4 quantization.

Can RTX 4000 Ada 20GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on RTX 4000 Ada 20GB receives a F grade with 7.5 tok/s and 4K context.

What context window can Granite 4.1 30B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 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.

See all results for RTX 4000 Ada 20GB See all hardware for Granite 4.1 30B

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