Can CodeLlama 13B Instruct run on RTX 5000 Ada 32GB?

YES — Runs Great

A81Great

Estimated from fit model

CodeLlama 13B Instruct needs ~24.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 24.5 GB, 58.1 tok/s, Runs well

24.5 GB required32.0 GB available

77% VRAM used

Fit status

Runs well

Decode

58.1 tok/s

TTFT

3332 ms

Safe context

16K

Memory

24.5 GB / 32.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on RTX 5000 Ada 32GB
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: 58.1 tok/s decode · 3.3s TTFT (warm) · 145 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	A	Runs well	58.1 tok/s	1817 ms	16K
Coding	A	Runs well	58.1 tok/s	3332 ms	16K
Agentic Coding	B	Very compromised (needs ~1 GB host RAM)	32.6 tok/s	8644 ms	16K
Reasoning	A	Runs well	58.1 tok/s	3937 ms	16K
RAG	B	Very compromised (needs ~1 GB host RAM)	32.6 tok/s	10805 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B69
Q3_K_S	3	6.4 GB	Low	B69
NVFP4	4	7.3 GB	Medium	B70
Q4_K_M	4	7.9 GB	Medium	B70
Q5_K_M	5	9.4 GB	High	A71
Q6_K	6	10.7 GB	High	A71
Q8_0	8	13.9 GB	Very High	A73
F16Best for your GPU	16	26.7 GB	Maximum	A74

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 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
Qwen 3.5 27B	27B	S	30.2 tok/s
Qwen 3.6 27B	27B	S	30.3 tok/s
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run CodeLlama 13B Instruct?

Yes, RTX 5000 Ada 32GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 58.1 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 24.5 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, CodeLlama 13B Instruct achieves approximately 58.1 tokens per second decode speed with a time-to-first-token of 3332ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RTX 5000 Ada 32GB receives a A grade with 58.1 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, 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 5000 Ada 32GB See all hardware for CodeLlama 13B Instruct

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