Can StarCoder 15B run on Radeon Pro W7900 48GB?

YES — Runs Great

A79Great

○Estimated from fit model

StarCoder 15B needs ~31.4 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q5_K_M quantization, expect ~48 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

Q5_K_M (High quality) — 31.4 GB, 48.1 tok/s, Runs well

31.4 GB required48.0 GB available

65% VRAM used

Fit status

Runs well

Decode

48.1 tok/s

TTFT

4021 ms

Safe context

Memory

31.4 GB / 48.0 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsStarCoder 15B on Radeon Pro W7900 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: 48.1 tok/s decode · 4.0s TTFT (warm) · 120 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	A	Runs well	48.1 tok/s	2193 ms	8K
Coding	A	Runs well	48.1 tok/s	4021 ms	8K
Agentic Coding	A	Runs with offload	48.1 tok/s	5849 ms	8K
Reasoning	A	Runs well	48.1 tok/s	4752 ms	8K
RAG	A	Runs with offload	48.1 tok/s	7311 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	B67
Q3_K_S	3	7.4 GB	Low	B67
NVFP4	4	8.4 GB	Medium	B68
Q4_K_M	4	9.2 GB	Medium	B68
Q5_K_M	5	10.8 GB	High	B68
Q6_K	6	12.3 GB	High	B69
Q8_0	8	16.1 GB	Very High	B70
F16Best for your GPU	16	30.7 GB	Maximum	A73

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Your hardware

More models your Radeon Pro W7900 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	77.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	33.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	33.5 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	64.8 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	79.7 tok/s

Frequently asked questions

See all results for Radeon Pro W7900 48GB See all hardware for StarCoder 15B

URL: https://willitrunai.com/can-run/starcoder-15b-on-radeon-pro-w7900-48gb

⇱ StarCoder 15B on Radeon Pro W7900 48GB? YES