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URL: https://willitrunai.com/can-run/starcoder-7b-on-rx-5700-xt-8gb

⇱ StarCoder 7B on RX 5700 XT 8GB? No — Alternatives

Can StarCoder 7B run on RX 5700 XT 8GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

StarCoder 7B needs ~13.3 GB but RX 5700 XT 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.3 GB, exceeds 8.0 GB available
13.3 GB required8.0 GB available
166% VRAM needed

5.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.0 tok/s

TTFT

13781 ms

Safe context

4K

Memory

13.3 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB
KV Cache7.3 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder 7B on RX 5700 XT 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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.3 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy27.7 tok/s3815 ms4K
CodingFToo heavy14.0 tok/s13781 ms4K
Agentic CodingFToo heavy8.2 tok/s34410 ms4K
ReasoningFToo heavy14.0 tok/s16287 ms4K
RAGFToo heavy8.2 tok/s43013 ms4K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA77
Q3_K_S
3
3.4 GB
LowA77
NVFP4
4
3.9 GB
MediumA77
Q4_K_M
4
4.3 GB
MediumA76
Q5_K_MBest for your GPU
5
5.0 GB
HighA76
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Upgrade options

Hardware that runs StarCoder 7B well

RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.39.1 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.

~$329 MSRP

RX 9060 XT 16GBBest value
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.47.2 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.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade
12 GB VRAM (+4)
B
Makes the model fit on the accelerator instead of staying completely out of reach.34.5 tok/s decode

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

Raises estimated decode speed by about 146%.

~$449 MSRP

RX 7900 XT 20GBBiggest leap
20 GB VRAM (+12)800 GB/s (+352)
A
Makes the model fit on the accelerator instead of staying completely out of reach.98 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.

~$899 MSRP

Frequently asked questions

See all results for RX 5700 XT 8GBSee all hardware for StarCoder 7B