Can starcoder2 15b instruct v0.1 run on RTX 2000 Ada 16GB?

YES — Tight Fit

C49Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.7 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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) — 13.7 GB, 23.9 tok/s, Tight fit

13.7 GB required16.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

23.9 tok/s

TTFT

8093 ms

Safe context

37K

Memory

13.7 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX 2000 Ada 16GB
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: 23.9 tok/s decode · 8.1s TTFT (warm) · 60 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	23.9 tok/s	4414 ms	37K
Coding	C	Tight fit	23.9 tok/s	8093 ms	37K
Agentic Coding	C	Runs with offload	23.9 tok/s	11772 ms	37K
Reasoning	C	Tight fit	23.9 tok/s	9565 ms	37K
RAG	C	Runs with offload	23.9 tok/s	14715 ms	37K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C49
Q3_K_S	3	7.4 GB	Low	C51
NVFP4	4	8.4 GB	Medium	C51
Q4_K_M	4	9.2 GB	Medium	C51
Q5_K_M	5	10.8 GB	High	C51
Q6_KBest for your GPU	6	12.3 GB	High	C50
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-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Upgrade-Optionen

Hardware, die starcoder2 15b instruct v0.1 gut ausführt

RTX 4000 Ada 20GBGünstige Wahl

20 GB VRAM (+4)360 GB/s (+72)

Raises estimated decode speed by about 28%.30.7 tok/s Dekodierung

Raises estimated decode speed by about 28%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,250 MSRP

RTX 3090 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+8)936 GB/s (+648)

Raises estimated decode speed by about 200%.71.6 tok/s Dekodierung

Raises estimated decode speed by about 200%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,499 MSRP

RTX 4090 24GBNVIDIA-Upgrade

24 GB VRAM (+8)1008 GB/s (+720)

Raises estimated decode speed by about 250%.83.7 tok/s Dekodierung

Raises estimated decode speed by about 250%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,599 MSRP

Frequently asked questions

Can RTX 2000 Ada 16GB run starcoder2 15b instruct v0.1?

Yes, RTX 2000 Ada 16GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 23.9 tok/s.

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

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.7 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 2000 Ada 16GB?

On RTX 2000 Ada 16GB, starcoder2 15b instruct v0.1 achieves approximately 23.9 tokens per second decode speed with a time-to-first-token of 8093ms using Q4_K_M quantization.

Can RTX 2000 Ada 16GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 2000 Ada 16GB receives a C grade with 23.9 tok/s and 37K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 2000 Ada 16GB?

On RTX 2000 Ada 16GB, starcoder2 15b instruct v0.1 can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2000 Ada 16GB See all hardware for starcoder2 15b instruct v0.1

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