Can Granite Code 8B run on RX 5600 XT 6GB?

YES — With Q2_K

B65Good

○Estimated from fit model

Granite Code 8B needs ~6.6 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q2_K quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Granite Code 8B at Q4_K_M needs 8.3 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at Q2_K (6.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.3 GB, exceeds 6.0 GB available

8.3 GB required6.0 GB available

138% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.4 tok/s

TTFT

15623 ms

Safe context

Memory

8.3 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights4.9 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 feelsGranite Code 8B 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: 12.4 tok/s decode · 15.6s TTFT (warm) · 31 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 0.3 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	15.0 tok/s	7046 ms	4K
Coding	F	Too heavy	11.5 tok/s	16794 ms	4K
Agentic Coding	F	Too heavy	7.4 tok/s	38053 ms	4K
Reasoning	F	Too heavy	12.4 tok/s	18463 ms	4K
RAG	F	Too heavy	7.4 tok/s	47566 ms	4K

Quantization options

How Granite Code 8B (8B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	A79
Q3_K_S	3	3.9 GB	Low	F0

Get started

Copy-paste commands to run Granite Code 8B on your machine.

Run

ollama run granite-code:8b

Upgrade options

Hardware that runs Granite Code 8B well

RX 580 8GBBudget pick

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.15.4 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.26.2 tok/s decode

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

Raises estimated decode speed by about 111%.

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.24.1 tok/s decode

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

Raises estimated decode speed by about 94%.

~$269 MSRP

👁 NVIDIA
RTX 3080 Ti 12GBBiggest leap

12 GB VRAM (+6)912 GB/s (+624)

Makes the model fit on the accelerator instead of staying completely out of reach.96 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.

~$1,199 MSRP

Frequently asked questions

See all results for RX 5600 XT 6GB See all hardware for Granite Code 8B

URL: https://willitrunai.com/can-run/granite-code-8b-on-rx-5600-xt-6gb

⇱

Can Granite Code 8B run on RX 5600 XT 6GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Granite Code 8B well

Frequently asked questions