Can StarCoder2 15B run on Radeon PRO W7700 16GB?

YES — Tight Fit

C52Usable

○Estimated from fit model

StarCoder2 15B needs ~14.5 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q5_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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

Q5_K_M (High quality) — 14.5 GB, 35.0 tok/s, Tight fit

14.5 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

35.0 tok/s

TTFT

5525 ms

Safe context

16K

Memory

14.5 GB / 16.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on Radeon PRO W7700 16GB
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: 35.0 tok/s decode · 5.5s TTFT (warm) · 88 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	Tight fit	35.0 tok/s	3014 ms	16K
Coding	C	Tight fit	35.0 tok/s	5525 ms	16K
Agentic Coding	C	Runs with offload	35.0 tok/s	8037 ms	16K
Reasoning	C	Tight fit	35.0 tok/s	6530 ms	16K
RAG	C	Runs with offload	35.0 tok/s	10046 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C51
Q3_K_S	3	7.4 GB	Low	C53
NVFP4	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	C53
Q5_K_M	5	10.8 GB	High	C52
Q6_KBest for your GPU	6	12.3 GB	High	C52
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "bigcode/starcoder2-15b" \
 --hf-file "starcoder2-15b-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options