Can StarCoder2 15B run on RX 6600 8GB?

YES — With Q2_K

D36Poor

Estimated from fit model

StarCoder2 15B needs ~9.3 GB VRAM. RX 6600 8GB has 8.0 GB. With Q2_K quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

StarCoder2 15B at Q4_K_M needs 12.6 GB — too much for RX 6600 8GB (8.0 GB). Runs at Q2_K (9.3 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.6 GB, exceeds 8.0 GB available

12.6 GB required8.0 GB available

158% VRAM needed

4.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.5 tok/s

TTFT

56061 ms

Safe context

Memory

12.6 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 15B on RX 6600 8GB
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: 3.5 tok/s decode · 56.1s TTFT (warm) · 9 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.0 tok/s	26264 ms	4K
Coding	F	Too heavy	3.5 tok/s	56061 ms	4K
Agentic Coding	F	Too heavy	2.6 tok/s	107328 ms	4K
Reasoning	F	Too heavy	3.5 tok/s	66254 ms	4K
RAG	F	Too heavy	2.6 tok/s	134160 ms	4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	F0
Q3_K_S	3	7.4 GB	Low	F0
NVFP4	4	8.4 GB	Medium	F0
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
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

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien StarCoder2 15B

RX 7600 XT 16GBOpción económica

16 GB VRAM (+8)288 GB/s (+64)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.18.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$329 MSRP

RX 9060 XT 16GBMejor relación calidad-precio

16 GB VRAM (+8)320 GB/s (+96)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.22 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$349 MSRP

RX 7700 XT 12GBMejora AMD

12 GB VRAM (+4)432 GB/s (+208)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.17.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 411%.

~$449 MSRP

RTX A4500 20GBMayor salto

20 GB VRAM (+12)640 GB/s (+416)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.54.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$2,000 MSRP

Frequently asked questions

Can RX 6600 8GB run StarCoder2 15B?

Yes, RX 6600 8GB can run StarCoder2 15B at Q2_K quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 12.6 GB which exceeds available memory, but at Q2_K it needs only 9.3 GB. Expected decode speed: 8.7 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 12.6 GB at Q4_K_M quantization. On RX 6600 8GB, it fits at Q2_K using 9.3 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q4_K_M, but on RX 6600 8GB the best fitting quantization is Q2_K, which uses 9.3 GB.

What speed will StarCoder2 15B run at on RX 6600 8GB?

On RX 6600 8GB, StarCoder2 15B achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22262ms using Q2_K quantization.

Can RX 6600 8GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RX 6600 8GB receives a F grade with 3.5 tok/s and 4K context.

What context window can StarCoder2 15B use on RX 6600 8GB?

On RX 6600 8GB, StarCoder2 15B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on RX 6600 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 6600 8GB See all hardware for StarCoder2 15B

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