Can Granite 4.1 30B run on NVIDIA L4 24GB?

Q: What should I upgrade first if Granite 4.1 30B feels slow on NVIDIA L4 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

B68Good

Estimated from fit model

Granite 4.1 30B needs ~25.8 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~7 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.8 GB, 7.4 tok/s, Runs with offload (needs ~1.3 GB host RAM)

25.8 GB required24.0 GB available

108% VRAM needed

1.8 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~1.3 GB host RAM)

Decode

7.4 tok/s

TTFT

26254 ms

Safe context

Memory

25.8 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B on NVIDIA L4 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: 7.4 tok/s decode · 26.3s 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 {ram} 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	11.5 tok/s	9219 ms	9K
Coding	B	Runs with offload	6.9 tok/s	28223 ms	9K
Agentic Coding	F	Too heavy	5.5 tok/s	51377 ms	9K
Reasoning	B	Runs with offload (needs ~1.3 GB host RAM)	7.4 tok/s	31027 ms	9K
RAG	F	Too heavy	5.5 tok/s	64222 ms	9K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on NVIDIA L4 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

Upgrade options

Hardware that runs Granite 4.1 30B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Removes host-memory offload, which is usually the single biggest latency and throughput win.70.5 tok/s decode

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

Raises estimated decode speed by about 853%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Removes host-memory offload, which is usually the single biggest latency and throughput win.44.2 tok/s decode

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

Raises estimated decode speed by about 497%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+276)

Removes host-memory offload, which is usually the single biggest latency and throughput win.27.1 tok/s decode

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

Raises estimated decode speed by about 266%.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Granite 4.1 30B?

Yes, NVIDIA L4 24GB can run Granite 4.1 30B with a B grade (Runs with offload). Expected decode speed: 6.9 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 25.8 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Granite 4.1 30B achieves approximately 6.9 tokens per second decode speed with a time-to-first-token of 28223ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on NVIDIA L4 24GB receives a B grade with 6.9 tok/s and 9K context.

What context window can Granite 4.1 30B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Granite 4.1 30B can safely use up to 9K 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 NVIDIA L4 24GB?

See all results for NVIDIA L4 24GB See all hardware for Granite 4.1 30B

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