Can starcoder2 15b instruct v0.1 run on NVIDIA A2 16GB?

YES — Tight Fit

C48Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.7 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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, 17.0 tok/s, Tight fit

13.7 GB required16.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

17.0 tok/s

TTFT

11355 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 NVIDIA A2 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: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 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	17.0 tok/s	6194 ms	37K
Coding	C	Tight fit	17.0 tok/s	11355 ms	37K
Agentic Coding	C	Runs with offload	17.0 tok/s	16517 ms	37K
Reasoning	C	Tight fit	17.0 tok/s	13420 ms	37K
RAG	C	Runs with offload	17.0 tok/s	20646 ms	37K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on NVIDIA A2 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

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

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 81%.30.7 tok/s decode

Raises estimated decode speed by about 81%.

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 321%.71.6 tok/s decode

Raises estimated decode speed by about 321%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 263%.61.7 tok/s decode

Raises estimated decode speed by about 263%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run starcoder2 15b instruct v0.1?

Yes, NVIDIA A2 16GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 17.0 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, starcoder2 15b instruct v0.1 achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11355ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on NVIDIA A2 16GB receives a C grade with 17.0 tok/s and 37K context.

What context window can starcoder2 15b instruct v0.1 use on NVIDIA A2 16GB?

On NVIDIA A2 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 NVIDIA A2 16GB See all hardware for starcoder2 15b instruct v0.1

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