Can StarCoder 15B run on Radeon Pro W7800 32GB?

YES — Tight Fit

A76Great

○Estimated from fit model

StarCoder 15B needs ~29.8 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q5_K_M quantization, expect ~32 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 29.8 GB, 32.1 tok/s, Tight fit

29.8 GB required32.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

32.1 tok/s

TTFT

6032 ms

Safe context

Memory

29.8 GB / 32.0 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsStarCoder 15B on Radeon Pro W7800 32GB
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: 32.1 tok/s decode · 6.0s TTFT (warm) · 80 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	32.1 tok/s	3290 ms	8K
Coding	A	Tight fit	32.1 tok/s	6032 ms	8K
Agentic Coding	F	Too heavy	12.0 tok/s	23416 ms	8K
Reasoning	A	Tight fit	32.1 tok/s	7129 ms	8K
RAG	F	Too heavy	12.0 tok/s	29270 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	B69
Q3_K_S	3	7.4 GB	Low	B70
NVFP4	4

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 W7800 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	51.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	22.3 tok/s

Frequently asked questions

See all results for Radeon Pro W7800 32GB See all hardware for StarCoder 15B

URL: https://willitrunai.com/can-run/starcoder-15b-on-radeon-pro-w7800-32gb

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