Can starcoder2 15b instruct v0.1 run on RX 7600 XT 16GB?

YES — Tight Fit

C48Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.4 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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, 18.3 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

18.3 tok/s

TTFT

10605 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 RX 7600 XT 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: 18.3 tok/s decode · 10.6s TTFT (warm) · 46 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	18.3 tok/s	5784 ms	40K
Coding	C	Tight fit	18.3 tok/s	10605 ms	40K
Agentic Coding	C	Tight fit	18.3 tok/s	15425 ms	40K
Reasoning	C	Tight fit	18.3 tok/s	12533 ms	40K
RAG	C	Tight fit	18.3 tok/s	19282 ms	40K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on RX 7600 XT 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-lmstudio-community--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 (+512)

Raises estimated decode speed by about 187%.52.5 tok/s decode

Raises estimated decode speed by about 187%.

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 (+672)

Raises estimated decode speed by about 313%.75.5 tok/s decode

Raises estimated decode speed by about 313%.

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

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+352)

Raises estimated decode speed by about 126%.41.3 tok/s decode

Raises estimated decode speed by about 126%.

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

~$1,899 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run starcoder2 15b instruct v0.1?

Yes, RX 7600 XT 16GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 18.3 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 RX 7600 XT 16GB?

On RX 7600 XT 16GB, starcoder2 15b instruct v0.1 achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10605ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on RX 7600 XT 16GB receives a C grade with 18.3 tok/s and 40K context.

What context window can starcoder2 15b instruct v0.1 use on RX 7600 XT 16GB?

On RX 7600 XT 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 RX 7600 XT 16GB See all hardware for starcoder2 15b instruct v0.1

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