Can starcoder2 15b instruct v0.1 run on Radeon PRO W7700 16GB?

YES — Tight Fit

C51Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.4 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 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

Q4_K_M (Medium quality) — 13.4 GB, 37.1 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

37.1 tok/s

TTFT

5213 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 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: 37.1 tok/s decode · 5.2s TTFT (warm) · 93 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	Runs well	37.1 tok/s	2843 ms	40K
Coding	C	Tight fit	37.1 tok/s	5213 ms	40K
Agentic Coding	C	Tight fit	37.1 tok/s	7582 ms	40K
Reasoning	C	Tight fit	37.1 tok/s	6160 ms	40K
RAG	C	Tight fit	37.1 tok/s	9477 ms	40K

Quantization options

How starcoder2 15b instruct v0.1 (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	C49
Q3_K_S	3	7.4 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Raises estimated decode speed by about 42%.52.5 tok/s decode

Raises estimated decode speed by about 42%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Raises estimated decode speed by about 104%.75.5 tok/s decode

Raises estimated decode speed by about 104%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

AMD Instinct MI100 32GBAMD upgrade

32 GB VRAM (+16)1228 GB/s (+652)

Raises estimated decode speed by about 135%.87.2 tok/s decode

Raises estimated decode speed by about 135%.

Adds memory headroom for longer context windows and future model growth.

~$11,500 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run starcoder2 15b instruct v0.1?

Yes, Radeon PRO W7700 16GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 37.1 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, starcoder2 15b instruct v0.1 achieves approximately 37.1 tokens per second decode speed with a time-to-first-token of 5213ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on Radeon PRO W7700 16GB receives a C grade with 37.1 tok/s and 40K context.

What context window can starcoder2 15b instruct v0.1 use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, starcoder2 15b instruct v0.1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GB See all hardware for starcoder2 15b instruct v0.1

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