Can CodeLlama 13B Instruct run on NVIDIA L4 24GB?

Q: What should I upgrade first if CodeLlama 13B Instruct feels slow on NVIDIA L4 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

A75Great

Estimated from fit model

CodeLlama 13B Instruct needs ~23.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 23.4 GB, 25.8 tok/s, Runs with offload

23.4 GB required24.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

25.8 tok/s

TTFT

7498 ms

Safe context

16K

Memory

23.4 GB / 24.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	25.8 tok/s	4090 ms	16K
Coding	A	Runs with offload	24.6 tok/s	7873 ms	16K
Agentic Coding	F	Too heavy	8.4 tok/s	33436 ms	16K
Reasoning	A	Runs with offload	25.8 tok/s	8861 ms	16K
RAG	F	Too heavy	8.4 tok/s	41794 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A71
Q3_K_S	3	6.4 GB	Low	A71
NVFP4	4

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Your hardware

More models your NVIDIA L4 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	21.2 tok/s
Qwen 3.5 27B	27B	S	8.9 tok/s

Frequently asked questions

Can NVIDIA L4 24GB run CodeLlama 13B Instruct?

Yes, NVIDIA L4 24GB can run CodeLlama 13B Instruct with a A grade (Runs with offload). Expected decode speed: 24.6 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, CodeLlama 13B Instruct achieves approximately 24.6 tokens per second decode speed with a time-to-first-token of 7873ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on NVIDIA L4 24GB receives a A grade with 24.6 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, CodeLlama 13B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 13B Instruct feels slow on NVIDIA L4 24GB?

See all results for NVIDIA L4 24GB See all hardware for CodeLlama 13B Instruct

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