Can starcoder2 15b instruct v0.1 run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Runs Great

C46Usable

○Estimated from fit model

starcoder2 15b instruct v0.1 needs ~21.7 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~165 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) — 21.7 GB, 164.5 tok/s, Runs well

21.7 GB required96.0 GB available

23% VRAM used

Fit status

Runs well

Decode

164.5 tok/s

TTFT

1177 ms

Safe context

692K

Memory

21.7 GB / 96.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 164.5 tok/s decode · 1.2s TTFT (warm) · 411 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	164.5 tok/s	642 ms	692K
Coding	C	Runs well	164.5 tok/s	1177 ms	692K
Agentic Coding	C	Runs well	164.5 tok/s	1712 ms	692K
Reasoning	C	Runs well	164.5 tok/s	1391 ms	692K
RAG	C	Runs well	164.5 tok/s	2140 ms	692K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D39
Q3_K_S	3	7.4 GB	Low	D39
NVFP4	4	8.4 GB	Medium	D39
Q4_K_M	4	9.2 GB	Medium	D39
Q5_K_M	5	10.8 GB	High	D39
Q6_K	6	12.3 GB	High	D39
Q8_0	8	16.1 GB	Very High	D40
F16Best for your GPU	16	30.7 GB	Maximum	C42

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 6000 Blackwell Workstation Edition 96GB 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-6000-blackwell-96gb

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