Can StarCoder 15B run on RTX A4500 20GB?

Q: What should I upgrade first if StarCoder 15B 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.

YES — With Q2_K

B60Good

Estimated from fit model

StarCoder 15B needs ~23.7 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q2_K quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

StarCoder 15B at Q5_K_M needs 28.6 GB — too much for RTX A4500 20GB (20.0 GB). Runs at Q2_K (23.7 GB) with low quality.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 28.6 GB, exceeds 20.0 GB available

28.6 GB required20.0 GB available

143% VRAM needed

8.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.6 tok/s

TTFT

11666 ms

Safe context

Memory

28.6 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B 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: 16.6 tok/s decode · 11.7s TTFT (warm) · 42 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 20% 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 0.9 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	B	Runs with offload (needs ~0.7 GB host RAM)	30.9 tok/s	3418 ms	7K
Coding	F	Too heavy	16.6 tok/s	11666 ms	7K
Agentic Coding	F	Too heavy	7.1 tok/s	39819 ms	7K
Reasoning	F	Too heavy	16.6 tok/s	13787 ms	7K
RAG	F	Too heavy	7.1 tok/s	49774 ms	7K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A73
Q3_K_S	3	7.4 GB	Low	A74
NVFP4	4

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Upgrade options

Hardware that runs StarCoder 15B well

RTX 5090 32GBBudget pick

32 GB VRAM (+12)1792 GB/s (+1152)

Makes the model fit on the accelerator instead of staying completely out of reach.113.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+12)896 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.71.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.43.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$4,000 MSRP

Frequently asked questions

Can RTX A4500 20GB run StarCoder 15B?

Yes, RTX A4500 20GB can run StarCoder 15B at Q2_K quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q5_K_M requires 28.6 GB which exceeds available memory, but at Q2_K it needs only 23.7 GB. Expected decode speed: 38.1 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 28.6 GB at Q5_K_M quantization. On RTX A4500 20GB, it fits at Q2_K using 23.7 GB.

What is the best quantization for StarCoder 15B?

The recommended quantization is Q5_K_M, but on RTX A4500 20GB the best fitting quantization is Q2_K, which uses 23.7 GB.

What speed will StarCoder 15B run at on RTX A4500 20GB?

On RTX A4500 20GB, StarCoder 15B achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5087ms using Q2_K quantization.

Can RTX A4500 20GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on RTX A4500 20GB receives a F grade with 16.6 tok/s and 7K context.

What context window can StarCoder 15B use on RTX A4500 20GB?

On RTX A4500 20GB, StarCoder 15B can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX A4500 20GB See all hardware for StarCoder 15B

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