Can StarCoder 7B run on Radeon Pro W7800 32GB?

YES — Runs Great

A75Great

○Estimated from fit model

StarCoder 7B needs ~15.7 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 15.7 GB, 79.6 tok/s, Runs well

15.7 GB required32.0 GB available

49% VRAM used

Fit status

Runs well

Decode

79.6 tok/s

TTFT

2433 ms

Safe context

Memory

15.7 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsStarCoder 7B 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: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 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	79.6 tok/s	1327 ms	8K
Coding	A	Runs well	79.6 tok/s	2433 ms	8K
Agentic Coding	A	Runs well	79.6 tok/s	3538 ms	8K
Reasoning	A	Runs well	79.6 tok/s	2875 ms	8K
RAG	A	Runs well	79.6 tok/s	4423 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B66
Q3_K_S	3	3.4 GB	Low	B66
NVFP4	4	3.9 GB	Medium	B67
Q4_K_M	4	4.3 GB	Medium	B67
Q5_K_M	5	5.0 GB	High	B67
Q6_K	6	5.7 GB	High	B67
Q8_0	8	7.5 GB	Very High	B68
F16Best for your GPU	16	14.3 GB	Maximum	A71

Get started

Copy-paste commands to run StarCoder 7B on your machine.

Run

lms load starcoder-7b && lms server start

Your hardware

More models your Radeon Pro W7800 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	51.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	22.3 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	16.9 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	43.2 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	53.1 tok/s

Frequently asked questions

See all results for Radeon Pro W7800 32GB See all hardware for StarCoder 7B

URL: https://willitrunai.com/can-run/starcoder-7b-on-radeon-pro-w7800-32gb

⇱ StarCoder 7B on Radeon Pro W7800 32GB? YES