Can CodeLlama 13B Instruct run on RTX 4000 Ada 20GB?

BARELY — Tight on Memory

B64Good

○Estimated from fit model

CodeLlama 13B Instruct needs ~23.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 23.0 GB, 19.7 tok/s, Very compromised (needs ~1 GB host RAM)

23.0 GB required20.0 GB available

115% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

19.7 tok/s

TTFT

9817 ms

Safe context

12K

Memory

23.0 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on RTX 4000 Ada 20GB
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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	35.4 tok/s	2982 ms	12K
Coding	B	Very compromised (needs ~1 GB host RAM)	19.7 tok/s	9817 ms	12K
Agentic Coding	F	Too heavy	8.1 tok/s	34946 ms	12K
Reasoning	B	Very compromised (needs ~1 GB host RAM)	19.7 tok/s	11601 ms	12K
RAG	F	Too heavy	8.1 tok/s	43682 ms	12K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A72
Q3_K_S	3	6.4 GB	Low	A73
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

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

👁 NVIDIA
RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Removes host-memory offload, which is usually the single biggest latency and throughput win.81 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 311%.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Removes host-memory offload, which is usually the single biggest latency and throughput win.101.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 415%.

~$1,599 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Removes host-memory offload, which is usually the single biggest latency and throughput win.71.2 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 261%.

~$1,599 MSRP

Frequently asked questions

See all results for RTX 4000 Ada 20GB See all hardware for CodeLlama 13B Instruct

URL: https://willitrunai.com/can-run/codellama-13b-instruct-on-rtx-4000-ada-20gb

⇱

Can CodeLlama 13B Instruct run on RTX 4000 Ada 20GB?

BARELY — Tight on Memory

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs CodeLlama 13B Instruct well

Frequently asked questions