Can StarCoder 15B run on RX 7900 XT 20GB?

YES — With Q2_K

B60Good

Estimated from fit model

StarCoder 15B needs ~23.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q2_K quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: 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.

StarCoder 15B at Q5_K_M needs 28.6 GB — too much for RX 7900 XT 20GB (20.0 GB). Runs at Q2_K (23.7 GB) with low quality.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 28.6 GB, exceeds 20.0 GB available

28.6 GB required20.0 GB available

143% VRAM needed

8.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.0 tok/s

TTFT

12133 ms

Safe context

Memory

28.6 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B on RX 7900 XT 20GB
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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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 20% 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 0.9 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	B	Runs with offload (needs ~0.7 GB host RAM)	29.7 tok/s	3555 ms	7K
Coding	F	Too heavy	16.0 tok/s	12133 ms	7K
Agentic Coding	F	Too heavy	6.8 tok/s	41412 ms	7K
Reasoning	F	Too heavy	16.0 tok/s	14339 ms	7K
RAG	F	Too heavy	6.8 tok/s	51765 ms	7K

Inference speed

StarCoder 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~113 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	Q5_K_M	113.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	52.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	43.8	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	41.6	Fits
RTX 4090 24GB	24 GB	Q5_K_M	36.3	Too big
RX 7900 XTX 24GB	24 GB	Q5_K_M	32.8	Too big
RTX 3090 24GB	24 GB	Q5_K_M	31.1	Too big
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	30.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	30.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	22.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	20.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	18.3	Tight
RTX 4080 Super 16GB	16 GB	Q5_K_M	13.1	Too big
RTX 4070 12GB	12 GB	Q5_K_M	5.4	Too big
RTX 3060 12GB	12 GB	Q5_K_M	3.4	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.8	Too big

Estimates for single-stream decoding at Q5_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 15B (15B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A73
Q3_K_S	3	7.4 GB	Low	A74
NVFP4	4	8.4 GB	Medium	A75
Q4_K_M	4	9.2 GB	Medium	A75
Q5_K_M	5	10.8 GB	High	A76
Q6_K	6	12.3 GB	High	A76
Q8_0Best for your GPU	8	16.1 GB	Very High	A75
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

Opciones de mejora

Hardware que ejecuta bien StarCoder 15B

Radeon AI PRO R9700 32GBOpción económica

32 GB VRAM (+12)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.35.7 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.

~$1,899 MSRP

Radeon Pro W6800 32GBMejor relación calidad-precio

32 GB VRAM (+12)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.27.1 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.

~$2,249 MSRP

Radeon Pro W7800 32GBMejora AMD

32 GB VRAM (+12)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.32.1 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.

~$2,499 MSRP

NVIDIA A100 40GBMayor salto

40 GB VRAM (+20)1555 GB/s (+755)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.123.4 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.

~$10,000 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run StarCoder 15B?

Yes, RX 7900 XT 20GB can run StarCoder 15B at Q2_K quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q5_K_M requires 28.6 GB which exceeds available memory, but at Q2_K it needs only 23.7 GB. Expected decode speed: 36.6 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 28.6 GB at Q5_K_M quantization. On RX 7900 XT 20GB, it fits at Q2_K using 23.7 GB.

What is the best quantization for StarCoder 15B?

The recommended quantization is Q5_K_M, but on RX 7900 XT 20GB the best fitting quantization is Q2_K, which uses 23.7 GB.

What speed will StarCoder 15B run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, StarCoder 15B achieves approximately 36.6 tokens per second decode speed with a time-to-first-token of 5290ms using Q2_K quantization.

Can RX 7900 XT 20GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on RX 7900 XT 20GB receives a F grade with 16.0 tok/s and 7K context.

What context window can StarCoder 15B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, StarCoder 15B can safely use up to 8K tokens of context at Q2_K quantization. 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 RX 7900 XT 20GB?

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 7900 XT 20GB See all hardware for StarCoder 15B

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