Can CodeLlama 7B Instruct run on RTX 4500 Ada 24GB?

YES — Runs Great

A79Great

○Estimated from fit model

CodeLlama 7B Instruct needs ~15.7 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~80 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) — 15.7 GB, 79.9 tok/s, Runs well

15.7 GB required24.0 GB available

65% VRAM used

Fit status

Runs well

Decode

79.9 tok/s

TTFT

2422 ms

Safe context

16K

Memory

15.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on RTX 4500 Ada 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: 79.9 tok/s decode · 2.4s TTFT (warm) · 200 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	79.9 tok/s	1321 ms	16K
Coding	A	Runs well	79.9 tok/s	2422 ms	16K
Agentic Coding	A	Runs with offload	79.9 tok/s	3523 ms	16K
Reasoning	A	Runs well	79.9 tok/s	2863 ms	16K
RAG	A	Runs with offload	79.9 tok/s	4404 ms	16K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B68
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B68
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0	8	7.5 GB	Very High	A70
F16Best for your GPU	16	14.3 GB	Maximum	A73

Get started

Copy-paste commands to run CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Your hardware

More models your RTX 4500 Ada 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	51.6 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	22.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	22.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	53.4 tok/s
👁 Alibaba Qwen 3.5 9B	9B	S	66.8 tok/s

Frequently asked questions

See all results for RTX 4500 Ada 24GB See all hardware for CodeLlama 7B Instruct

URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-rtx-4500-ada-24gb

⇱ CodeLlama 7B Instruct on RTX 4500 Ada 24GB? YES