Can CodeLlama 13B Instruct run on RTX A4500 20GB?

BARELY — Tight on Memory

B66Good

Estimated from fit model

CodeLlama 13B Instruct needs ~23.0 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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, 35.1 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

35.1 tok/s

TTFT

5522 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 A4500 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: 35.1 tok/s decode · 5.5s TTFT (warm) · 88 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	63.0 tok/s	1678 ms	12K
Coding	B	Very compromised (needs ~1 GB host RAM)	35.1 tok/s	5522 ms	12K
Agentic Coding	F	Too heavy	14.3 tok/s	19657 ms	12K
Reasoning	B	Very compromised (needs ~1 GB host RAM)	35.1 tok/s	6526 ms	12K
RAG	F	Too heavy	14.3 tok/s	24571 ms	12K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX A4500 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

Upgrade-Optionen

Hardware, die CodeLlama 13B Instruct gut ausführt

RTX 3090 24GBGünstige Wahl

24 GB VRAM (+4)936 GB/s (+296)

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

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

Raises estimated decode speed by about 131%.

ca. $1,499 MSRP

RTX 4090 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+4)1008 GB/s (+368)

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

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

Raises estimated decode speed by about 189%.

ca. $1,599 MSRP

RTX A5500 24GBNVIDIA-Upgrade

24 GB VRAM (+4)768 GB/s (+128)

Removes host-memory offload, which is usually the single biggest latency and throughput win.75.5 tok/s Dekodierung

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

Raises estimated decode speed by about 115%.

ca. $3,200 MSRP

Frequently asked questions

Can RTX A4500 20GB run CodeLlama 13B Instruct?

Yes, RTX A4500 20GB can run CodeLlama 13B Instruct with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 35.1 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 A4500 20GB?

On RTX A4500 20GB, CodeLlama 13B Instruct achieves approximately 35.1 tokens per second decode speed with a time-to-first-token of 5522ms using Q4_K_M quantization.

Can RTX A4500 20GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RTX A4500 20GB receives a B grade with 35.1 tok/s and 12K context.

What context window can CodeLlama 13B Instruct use on RTX A4500 20GB?

On RTX A4500 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 A4500 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 A4500 20GB See all hardware for CodeLlama 13B Instruct

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