Can granite 8b code instruct 4k run on NVIDIA L4 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

granite 8b code instruct 4k needs ~9.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.4 GB, 40.0 tok/s, Runs well

9.4 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

40.0 tok/s

TTFT

4845 ms

Safe context

265K

Memory

9.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k 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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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	40.0 tok/s	2643 ms	265K
Coding	C	Runs well	40.0 tok/s	4845 ms	265K
Agentic Coding	C	Runs well	40.0 tok/s	7047 ms	265K
Reasoning	C	Runs well	40.0 tok/s	5726 ms	265K
RAG	C	Runs well	40.0 tok/s	8809 ms	265K

Quantization options

How granite 8b code instruct 4k (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	C44
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C45
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C46
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

Get started

Copy-paste commands to run granite 8b code instruct 4k on your machine.

Run

lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server start

Upgrade-Optionen

Hardware, die granite 8b code instruct 4k gut ausführt

RTX 5090 32GBGünstige Wahl

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

Raises estimated decode speed by about 180%.112 tok/s Dekodierung

Raises estimated decode speed by about 180%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

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

Raises estimated decode speed by about 180%.112 tok/s Dekodierung

Raises estimated decode speed by about 180%.

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

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

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

Raises estimated decode speed by about 136%.94.4 tok/s Dekodierung

Raises estimated decode speed by about 136%.

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

ca. $4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run granite 8b code instruct 4k?

Yes, NVIDIA L4 24GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 40.0 tok/s.

How much VRAM does granite 8b code instruct 4k need?

granite 8b code instruct 4k (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for granite 8b code instruct 4k?

The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.

What speed will granite 8b code instruct 4k run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, granite 8b code instruct 4k achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on NVIDIA L4 24GB receives a C grade with 40.0 tok/s and 265K context.

What context window can granite 8b code instruct 4k use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, granite 8b code instruct 4k can safely use up to 265K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for granite 8b code instruct 4k

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