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	7.3 GB	Medium	A74
Q4_K_M	4	7.9 GB	Medium	A74
Q5_K_M	5	9.4 GB	High	A75
Q6_K	6	10.7 GB	High	A76
Q8_0Best for your GPU	8	13.9 GB	Very High	A75
F16	16	26.7 GB	Maximum	F0

Get started

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

Run

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

Opciones de mejora

Hardware que ejecuta bien CodeLlama 13B Instruct

RTX 3090 24GBOpción económica

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

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.81 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 311%.

~$1,499 MSRP

RTX 4090 24GBMejor relación calidad-precio

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

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.101.4 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 415%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBMejora NVIDIA

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

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.71.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 261%.

~$1,599 MSRP

Frequently asked questions

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

Yes, RTX 4000 Ada 20GB can run CodeLlama 13B Instruct with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 19.7 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 23.0 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, CodeLlama 13B Instruct achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9817ms using Q4_K_M quantization.

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

For coding workloads, CodeLlama 13B Instruct on RTX 4000 Ada 20GB receives a B grade with 19.7 tok/s and 12K context.

What context window can CodeLlama 13B Instruct use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, CodeLlama 13B Instruct can safely use up to 12K 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 RTX 4000 Ada 20GB?

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.

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

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