Can Granite 3.1 8B run on NVIDIA L4 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

Granite 3.1 8B needs ~10.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 10.1 GB, 49.4 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

49.4 tok/s

TTFT

3919 ms

Safe context

128K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B 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: 49.4 tok/s decode · 3.9s TTFT (warm) · 124 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	49.4 tok/s	2138 ms	128K
Coding	C	Runs well	49.4 tok/s	3919 ms	128K
Agentic Coding	B	Runs well	49.4 tok/s	5700 ms	128K
Reasoning	C	Runs well	49.4 tok/s	4632 ms	128K
RAG	B	Runs well	49.4 tok/s	7126 ms	128K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C49
NVFP4	4	4.5 GB	Medium	C50
Q4_K_M	4	4.9 GB	Medium	C50
Q5_K_M	5	5.8 GB	High	C51
Q6_K	6	6.6 GB	High	C51
Q8_0	8	8.6 GB	Very High	C52
F16Best for your GPU	16	16.4 GB	Maximum	C54

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Opções de upgrade

Hardware que roda bem Granite 3.1 8B

RTX 5090 32GBOpção econômica

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

Raises estimated decode speed by about 208%.152 tok/s decodificação

Raises estimated decode speed by about 208%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

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

Raises estimated decode speed by about 127%.112 tok/s decodificação

Raises estimated decode speed by about 127%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

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

Raises estimated decode speed by about 127%.112 tok/s decodificação

Raises estimated decode speed by about 127%.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Granite 3.1 8B?

Yes, NVIDIA L4 24GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 49.4 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Granite 3.1 8B achieves approximately 49.4 tokens per second decode speed with a time-to-first-token of 3919ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on NVIDIA L4 24GB receives a C grade with 49.4 tok/s and 128K context.

What context window can Granite 3.1 8B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Granite 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for Granite 3.1 8B

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