Can starcoder2 15b instruct v0.1 run on RTX A2000 12GB?

BARELY — Tight on Memory

D38Poor

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.0 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~16 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) — 13.0 GB, 15.5 tok/s, Very compromised (needs ~0.7 GB host RAM)

13.0 GB required12.0 GB available

108% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

15.5 tok/s

TTFT

12460 ms

Safe context

Memory

13.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX A2000 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: 15.5 tok/s decode · 12.5s TTFT (warm) · 39 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 0.7 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	C	Runs with offload (needs ~0.1 GB host RAM)	18.0 tok/s	5865 ms	7K
Coding	D	Very compromised (needs ~0.7 GB host RAM)	15.5 tok/s	12460 ms	7K
Agentic Coding	F	Too heavy	11.9 tok/s	23665 ms	7K
Reasoning	D	Very compromised (needs ~0.7 GB host RAM)	15.5 tok/s	14725 ms	7K
RAG	F	Too heavy	11.9 tok/s	29581 ms	7K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C52
Q3_K_S	3	7.4 GB	Low	C51
NVFP4Best for your GPU	4	8.4 GB	Medium	C51
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien starcoder2 15b instruct v0.1

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+4)448 GB/s (+160)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.28.7 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 85%.

~$449 MSRP

RTX 4060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.23.5 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 52%.

~$499 MSRP

RTX 2000 Ada 16GBMejora NVIDIA

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.23.9 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 54%.

~$625 MSRP

Frequently asked questions

Can RTX A2000 12GB run starcoder2 15b instruct v0.1?

Yes, RTX A2000 12GB can run starcoder2 15b instruct v0.1 with a D grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 15.5 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.0 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on RTX A2000 12GB?

On RTX A2000 12GB, starcoder2 15b instruct v0.1 achieves approximately 15.5 tokens per second decode speed with a time-to-first-token of 12460ms using Q4_K_M quantization.

Can RTX A2000 12GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX A2000 12GB receives a D grade with 15.5 tok/s and 7K context.

What context window can starcoder2 15b instruct v0.1 use on RTX A2000 12GB?

On RTX A2000 12GB, starcoder2 15b instruct v0.1 can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on RTX A2000 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.

See all results for RTX A2000 12GB See all hardware for starcoder2 15b instruct v0.1

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