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URL: https://willitrunai.com/can-run/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-rx-5600-xt-6gb

⇱ granite 8b code instruct 4k on RX 5600 XT 6GB? No — Alterna…

Can granite 8b code instruct 4k run on RX 5600 XT 6GB?

YES — With NVFP4

D39Poor
○Estimated from fit model

granite 8b code instruct 4k needs ~6.9 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With NVFP4 quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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.

granite 8b code instruct 4k at Q4_K_M needs 7.3 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at NVFP4 (6.9 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.3 GB, exceeds 6.0 GB available
7.3 GB required6.0 GB available
122% VRAM needed

1.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.2 tok/s

TTFT

12774 ms

Safe context

4K

Memory

7.3 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB
KV Cache0.9 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 8b code instruct 4k 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: 15.2 tok/s decode · 12.8s TTFT (warm) · 38 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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~0.6 GB host RAM)17.4 tok/s6061 ms4K
CodingFToo heavy15.2 tok/s12774 ms4K
Agentic CodingFToo heavy11.8 tok/s23948 ms4K
ReasoningFToo heavy15.2 tok/s15097 ms4K
RAGFToo heavy11.8 tok/s29935 ms4K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
3.1 GB
LowC54
Q3_K_S
3
3.9 GB
LowF0
NVFP4
4
4.5 GB
MediumF0
Q4_K_M
4
4.9 GB
MediumF0
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run granite 8b code instruct 4k on your machine.

Run

lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server start

Upgrade options

Hardware that runs granite 8b code instruct 4k well

RX 580 8GBBudget pick
8 GB VRAM (+2)
C
Makes the model fit on the accelerator instead of staying completely out of reach.22.6 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)
C
Makes the model fit on the accelerator instead of staying completely out of reach.37.2 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.

~$249 MSRP

RX 7600 8GBAMD upgrade
8 GB VRAM (+2)
C
Makes the model fit on the accelerator instead of staying completely out of reach.34.2 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.

~$269 MSRP

👁 NVIDIA
RTX 3080 10GBBiggest leap
10 GB VRAM (+4)760 GB/s (+472)
B
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.

~$699 MSRP

Frequently asked questions

See all results for RX 5600 XT 6GBSee all hardware for granite 8b code instruct 4k