Can StarCoder 15B run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

A76Great

Estimated from fit model

StarCoder 15B needs ~29.8 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q5_K_M quantization, expect ~36 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, 35.7 tok/s, Tight fit

29.8 GB required32.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

35.7 tok/s

TTFT

5429 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 AI PRO R9700 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: 35.7 tok/s decode · 5.4s TTFT (warm) · 89 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	35.7 tok/s	2961 ms	8K
Coding	A	Tight fit	35.7 tok/s	5429 ms	8K
Agentic Coding	F	Too heavy	13.9 tok/s	20231 ms	8K
Reasoning	A	Tight fit	35.7 tok/s	6416 ms	8K
RAG	F	Too heavy	13.9 tok/s	25289 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on Radeon AI PRO R9700 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	8.4 GB	Medium	A70
Q4_K_M	4	9.2 GB	Medium	A71
Q5_K_M	5	10.8 GB	High	A71
Q6_K	6	12.3 GB	High	A72
Q8_0Best for your GPU	8	16.1 GB	Very High	A74
F16	16	30.7 GB	Maximum	F0

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 AI PRO R9700 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.5 27B	27B	S	24.8 tok/s
Qwen 3.6 27B	27B	S	24.8 tok/s
Qwen 3.6 35B A3B	35B	S	48 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	59.1 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run StarCoder 15B?

Yes, Radeon AI PRO R9700 32GB can run StarCoder 15B with a A grade (Tight fit). Expected decode speed: 35.7 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 29.8 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder 15B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StarCoder 15B achieves approximately 35.7 tokens per second decode speed with a time-to-first-token of 5429ms using Q5_K_M quantization.

Can Radeon AI PRO R9700 32GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on Radeon AI PRO R9700 32GB receives a A grade with 35.7 tok/s and 8K context.

What context window can StarCoder 15B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StarCoder 15B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 15B feels slow on Radeon AI PRO R9700 32GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Radeon AI PRO R9700 32GB See all hardware for StarCoder 15B

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