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URL: https://willitrunai.com/can-run/starcoder-15b-on-rx-7900-xt-20gb

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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
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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

7K

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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload29.7 tok/s3555 ms7K
CodingFToo heavy16.0 tok/s12133 ms7K
Agentic CodingFToo heavy6.8 tok/s41412 ms7K
ReasoningFToo heavy16.0 tok/s14339 ms7K
RAGFToo heavy6.8 tok/s51765 ms7K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowA73
Q3_K_S
3
7.4 GB
LowA74
NVFP4
4

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Upgrade options

Hardware that runs StarCoder 15B well

Radeon AI PRO R9700 32GBBudget pick
32 GB VRAM (+12)
A
Makes the model fit on the accelerator instead of staying completely out of reach.35.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Radeon Pro W6800 32GBBest value
32 GB VRAM (+12)
A
Makes the model fit on the accelerator instead of staying completely out of reach.27.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,249 MSRP

Radeon Pro W7800 32GBAMD upgrade
32 GB VRAM (+12)
A
Makes the model fit on the accelerator instead of staying completely out of reach.32.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

👁 NVIDIA
NVIDIA A100 40GBBiggest leap
40 GB VRAM (+20)1555 GB/s (+755)
A
Makes the model fit on the accelerator instead of staying completely out of reach.123.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$10,000 MSRP

Frequently asked questions

See all results for RX 7900 XT 20GBSee all hardware for StarCoder 15B
8.4 GB
Medium
A75
Q4_K_M
4
9.2 GB
MediumA75
Q5_K_M
5
10.8 GB
HighA76
Q6_K
6
12.3 GB
HighA76
Q8_0Best for your GPU
8
16.1 GB
Very HighA75
F16
16
30.7 GB
MaximumF0

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.