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URL: https://willitrunai.com/can-run/sqlcoder-7b-on-rx-5600-xt-6gb

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Can SQLCoder 7B run on RX 5600 XT 6GB?

YES — With Q3_K_S

B69Good
○Estimated from fit model

SQLCoder 7B needs ~6.9 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q3_K_S quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
Share:

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.

SQLCoder 7B at Q4_K_M needs 7.7 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at Q3_K_S (6.9 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.7 GB, exceeds 6.0 GB available
7.7 GB required6.0 GB available
128% VRAM needed

1.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.6 tok/s

TTFT

11648 ms

Safe context

4K

Memory

7.7 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsSQLCoder 7B on RX 5600 XT 6GB
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: 16.6 tok/s decode · 11.6s TTFT (warm) · 42 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 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised20.6 tok/s5139 ms4K
CodingFToo heavy15.5 tok/s12522 ms4K
Agentic CodingFToo heavy9.6 tok/s29276 ms4K
ReasoningFToo heavy15.5 tok/s14799 ms4K
RAGFToo heavy9.6 tok/s36595 ms4K

Quantization options

How SQLCoder 7B (7B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA84
Q3_K_SBest for your GPU
3
3.4 GB
LowA83

Get started

Copy-paste commands to run SQLCoder 7B on your machine.

Run

ollama run sqlcoder

Upgrade options

Hardware that runs SQLCoder 7B well

RX 580 8GBBudget pick
8 GB VRAM (+2)
A
Makes the model fit on the accelerator instead of staying completely out of reach.27.7 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.

~$229 MSRP

RX 9060 8GBBest value
8 GB VRAM (+2)
A
Makes the model fit on the accelerator instead of staying completely out of reach.45.7 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.

~$249 MSRP

RX 7600 8GBAMD upgrade
8 GB VRAM (+2)
A
Makes the model fit on the accelerator instead of staying completely out of reach.42.1 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.

~$269 MSRP

👁 NVIDIA
RTX 2080 Ti 11GBBiggest leap
11 GB VRAM (+5)616 GB/s (+328)
S
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.

~$999 MSRP

Frequently asked questions

See all results for RX 5600 XT 6GBSee all hardware for SQLCoder 7B
NVFP4
4
3.9 GB
Medium
F0
Q4_K_M
4
4.3 GB
MediumF0
Q5_K_M
5
5.0 GB
HighF0
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 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.