Can starcoder2 15b instruct v0.1 run on RTX 6000 Ada 48GB?

YES — Runs Great

C49Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~16.9 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~86 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 16.9 GB, 86.0 tok/s, Runs well

16.9 GB required48.0 GB available

35% VRAM used

Fit status

Runs well

Decode

86.0 tok/s

TTFT

2250 ms

Safe context

299K

Memory

16.9 GB / 48.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX 6000 Ada 48GB
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: 86.0 tok/s decode · 2.3s TTFT (warm) · 215 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	86.0 tok/s	1227 ms	299K
Coding	C	Runs well	86.0 tok/s	2250 ms	299K
Agentic Coding	C	Runs well	86.0 tok/s	3273 ms	299K
Reasoning	C	Runs well	86.0 tok/s	2659 ms	299K
RAG	C	Runs well	86.0 tok/s	4091 ms	299K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C41
Q3_K_S	3	7.4 GB	Low	C42
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

Frequently asked questions

Can RTX 6000 Ada 48GB run starcoder2 15b instruct v0.1?

Yes, RTX 6000 Ada 48GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 86.0 tok/s.

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

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 16.9 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 6000 Ada 48GB?

On RTX 6000 Ada 48GB, starcoder2 15b instruct v0.1 achieves approximately 86.0 tokens per second decode speed with a time-to-first-token of 2250ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RTX 6000 Ada 48GB receives a C grade with 86.0 tok/s and 299K context.

What context window can starcoder2 15b instruct v0.1 use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, starcoder2 15b instruct v0.1 can safely use up to 299K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for starcoder2 15b instruct v0.1

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