Can Granite 4.1 30B run on RTX A5000 24GB?

Q: What should I upgrade first if Granite 4.1 30B feels slow on RTX A5000 24GB?

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 Offload

A71Great

Estimated from fit model

Granite 4.1 30B needs ~25.5 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.5 GB, 20.8 tok/s, Runs with offload (needs ~1.1 GB host RAM)

25.5 GB required24.0 GB available

106% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~1.1 GB host RAM)

Decode

20.8 tok/s

TTFT

9291 ms

Safe context

10K

Memory

25.5 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B on RTX A5000 24GB
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: 20.8 tok/s decode · 9.3s TTFT (warm) · 52 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 1.1 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	Runs with offload	31.6 tok/s	3344 ms	10K
Coding	A	Runs with offload (needs ~1.1 GB host RAM)	20.8 tok/s	9291 ms	10K
Agentic Coding	F	Too heavy	15.4 tok/s	18242 ms	10K
Reasoning	A	Runs with offload (needs ~1.1 GB host RAM)	20.8 tok/s	10981 ms	10K
RAG	F	Too heavy	15.4 tok/s	22802 ms

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4

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 RTX A5000 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	81.3 tok/s
Qwen 3.6 35B A3B	35B	A	35 tok/s

Frequently asked questions

Can RTX A5000 24GB run Granite 4.1 30B?

Yes, RTX A5000 24GB can run Granite 4.1 30B with a A grade (Runs with offload (needs ~1.1 GB host RAM)). Expected decode speed: 20.8 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.5 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 RTX A5000 24GB?

On RTX A5000 24GB, Granite 4.1 30B achieves approximately 20.8 tokens per second decode speed with a time-to-first-token of 9291ms using Q4_K_M quantization.

Can RTX A5000 24GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on RTX A5000 24GB receives a A grade with 20.8 tok/s and 10K context.

What context window can Granite 4.1 30B use on RTX A5000 24GB?

On RTX A5000 24GB, Granite 4.1 30B can safely use up to 10K tokens of context. 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 A5000 24GB?

See all results for RTX A5000 24GB See all hardware for Granite 4.1 30B

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