Can CodeLlama 13B Instruct run on RTX 5090 Laptop 24GB?

Q: What should I upgrade first if CodeLlama 13B Instruct feels slow on RTX 5090 Laptop 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

A79Great

Estimated from fit model

CodeLlama 13B Instruct needs ~23.7 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: 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) — 23.7 GB, 94.9 tok/s, Runs with offload

23.7 GB required24.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

94.9 tok/s

TTFT

2040 ms

Safe context

16K

Memory

23.7 GB / 24.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on RTX 5090 Laptop 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: 94.9 tok/s decode · 2.0s TTFT (warm) · 237 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	94.9 tok/s	1113 ms	16K
Coding	A	Runs with offload	94.9 tok/s	2040 ms	16K
Agentic Coding	F	Too heavy	30.4 tok/s	9258 ms	16K
Reasoning	A	Runs with offload	94.9 tok/s	2411 ms	16K
RAG	F	Too heavy	30.4 tok/s	11572 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX 5090 Laptop 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 RTX 5090 Laptop 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	113.8 tok/s
Qwen 3.5 27B	27B	S	49.4 tok/s

Frequently asked questions

Can RTX 5090 Laptop 24GB run CodeLlama 13B Instruct?

Yes, RTX 5090 Laptop 24GB can run CodeLlama 13B Instruct with a A grade (Runs with offload). Expected decode speed: 94.9 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 23.7 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 RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, CodeLlama 13B Instruct achieves approximately 94.9 tokens per second decode speed with a time-to-first-token of 2040ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RTX 5090 Laptop 24GB receives a A grade with 94.9 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 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.

See all results for RTX 5090 Laptop 24GB See all hardware for CodeLlama 13B Instruct

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