Can StarCoder2 15B run on RTX 3500 Ada Laptop 12GB?

Q: What should I upgrade first if StarCoder2 15B feels slow on RTX 3500 Ada Laptop 12GB?

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.

BARELY — Tight on Memory

D33Poor

Estimated from fit model

StarCoder2 15B needs ~14.1 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q5_K_M quantization, expect ~12 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

Q5_K_M (High quality) — 14.1 GB, 13.8 tok/s, Very compromised (needs ~1.6 GB host RAM)

14.1 GB required12.0 GB available

118% VRAM needed

2.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.6 GB host RAM)

Decode

13.8 tok/s

TTFT

14058 ms

Safe context

Memory

14.1 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on RTX 3500 Ada Laptop 12GB
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: 13.8 tok/s decode · 14.1s TTFT (warm) · 34 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 {ram} 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	D	Very compromised (needs ~1.2 GB host RAM)	15.1 tok/s	6987 ms	4K
Coding	D	Very compromised	12.3 tok/s	15696 ms	4K
Agentic Coding	F	Too heavy	11.6 tok/s	24348 ms	4K
Reasoning	D	Very compromised (needs ~1.6 GB host RAM)	13.8 tok/s	16615 ms	4K
RAG	F	Too heavy	11.6 tok/s	30435 ms

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C54
Q3_K_S	3	7.4 GB	Low	C53
NVFP4Best for your GPU

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)448 GB/s (+112)

Removes host-memory offload, which is usually the single biggest latency and throughput win.27.1 tok/s decode

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

Raises estimated decode speed by about 96%.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.22.2 tok/s decode

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

Raises estimated decode speed by about 61%.

~$499 MSRP

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)896 GB/s (+560)

Removes host-memory offload, which is usually the single biggest latency and throughput win.55.9 tok/s decode

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

Raises estimated decode speed by about 305%.

~$749 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run StarCoder2 15B?

Yes, RTX 3500 Ada Laptop 12GB can run StarCoder2 15B with a D grade (Very compromised). Expected decode speed: 12.3 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.1 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, StarCoder2 15B achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15696ms using Q5_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RTX 3500 Ada Laptop 12GB receives a D grade with 12.3 tok/s and 4K context.

What context window can StarCoder2 15B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, StarCoder2 15B can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on RTX 3500 Ada Laptop 12GB?

See all results for RTX 3500 Ada Laptop 12GB See all hardware for StarCoder2 15B

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