Can StarCoder 7B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A76Great

Estimated from fit model

StarCoder 7B needs ~15.7 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 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, 88.4 tok/s, Runs well

15.7 GB required32.0 GB available

49% VRAM used

Fit status

Runs well

Decode

88.4 tok/s

TTFT

2189 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 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: 88.4 tok/s decode · 2.2s TTFT (warm) · 221 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	88.4 tok/s	1194 ms	8K
Coding	A	Runs well	88.4 tok/s	2189 ms	8K
Agentic Coding	A	Runs well	88.4 tok/s	3184 ms	8K
Reasoning	A	Runs well	88.4 tok/s	2587 ms	8K
RAG	A	Runs well	88.4 tok/s	3981 ms	8K

Inference speed

StarCoder 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Tight
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	48.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	30.2	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	11.4	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on Radeon AI PRO R9700 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 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	18.8 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 7B?

Yes, Radeon AI PRO R9700 32GB can run StarCoder 7B with a A grade (Runs well). Expected decode speed: 88.4 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder 7B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StarCoder 7B achieves approximately 88.4 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on Radeon AI PRO R9700 32GB receives a A grade with 88.4 tok/s and 8K context.

What context window can StarCoder 7B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for StarCoder 7B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

88.4 tok/s

1194 ms

Coding

Runs well

88.4 tok/s

2189 ms

Agentic Coding

Runs well

88.4 tok/s

3184 ms

Reasoning

Runs well

88.4 tok/s

2587 ms

RAG

Runs well

88.4 tok/s

3981 ms

StarCoder 7B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

98.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

98.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

98.0

Tight

RTX 3090 24GB

24 GB

Q4_K_M

98.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

98.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

98.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

87.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

87.8

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

56.2

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

51.5

Fits

RTX 4070 12GB

12 GB

Q4_K_M

48.0

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

45.3

Fits

RTX 3060 12GB

12 GB

Q4_K_M

30.2

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

11.4

Too big

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on Radeon AI PRO R9700 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

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

57.1 tok/s

Qwen 3.5 27B

27B

24.8 tok/s

Qwen 3.6 27B

27B

18.8 tok/s

Qwen 3.6 35B A3B

35B

48 tok/s

Qwen3-VL 30B A3B Instruct

30B

59.1 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run StarCoder 7B?

Yes, Radeon AI PRO R9700 32GB can run StarCoder 7B with a A grade (Runs well). Expected decode speed: 88.4 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder 7B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StarCoder 7B achieves approximately 88.4 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on Radeon AI PRO R9700 32GB receives a A grade with 88.4 tok/s and 8K context.

What context window can StarCoder 7B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.