Can starcoder2 15b instruct v0.1 run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

C49Usable

○Estimated from fit model

starcoder2 15b instruct v0.1 needs ~16.9 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~123 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, 123.4 tok/s, Runs well

16.9 GB required48.0 GB available

35% VRAM used

Fit status

Runs well

Decode

123.4 tok/s

TTFT

1569 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 PRO 5000 Blackwell 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: 123.4 tok/s decode · 1.6s TTFT (warm) · 309 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	123.4 tok/s	856 ms	299K
Coding	C	Runs well	123.4 tok/s	1569 ms	299K
Agentic Coding	C	Runs well	123.4 tok/s	2282 ms	299K
Reasoning	C	Runs well	123.4 tok/s	1854 ms	299K
RAG	C	Runs well	123.4 tok/s	2853 ms	299K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX PRO 5000 Blackwell 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	8.4 GB	Medium	C42
Q4_K_M	4	9.2 GB	Medium	C42
Q5_K_M	5	10.8 GB	High	C43
Q6_K	6	12.3 GB	High	C43
Q8_0	8	16.1 GB	Very High	C44
F16Best for your GPU	16	30.7 GB	Maximum	C48

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

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for starcoder2 15b instruct v0.1

URL: https://willitrunai.com/can-run/hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf-on-rtx-pro-5000-blackwell-48gb

⇱ starcoder2 15b instruct v0.1 on RTX PRO 5000 Blackwell 48GB…