Can Granite Code 20B run on RX 6700 XT 12GB?

YES — With Q2_K

B68Good

Estimated from fit model

Granite Code 20B needs ~13.1 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q2_K quantization, expect ~15 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 20B at Q4_K_M needs 17.5 GB — too much for RX 6700 XT 12GB (12.0 GB). Runs at Q2_K (13.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.5 GB, exceeds 12.0 GB available

17.5 GB required12.0 GB available

146% VRAM needed

5.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.0 tok/s

TTFT

32210 ms

Safe context

Memory

17.5 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 20B on RX 6700 XT 12GB
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: 6.0 tok/s decode · 32.2s TTFT (warm) · 15 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.3 tok/s	14379 ms	4K
Coding	F	Too heavy	6.0 tok/s	32210 ms	4K
Agentic Coding	F	Too heavy	4.2 tok/s	66573 ms	4K
Reasoning	F	Too heavy	6.0 tok/s	38067 ms	4K
RAG	F	Too heavy	4.2 tok/s	83216 ms	4K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	7.8 GB	Low	A81
Q3_K_S	3	9.8 GB	Low	F0
NVFP4	4	11.2 GB	Medium	F0
Q4_K_M	4	12.2 GB	Medium	F0
Q5_K_M	5	14.4 GB	High	F0
Q6_K	6	16.4 GB	High	F0
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Opciones de mejora

Hardware que ejecuta bien Granite Code 20B

RX 7600 XT 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.8.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 47%.

~$329 MSRP

RX 9060 XT 16GBMejora AMD

16 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.10.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 77%.

~$349 MSRP

RX 7900 XT 20GBOpción económica

20 GB VRAM (+8)800 GB/s (+416)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.42.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$899 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run Granite Code 20B?

Yes, RX 6700 XT 12GB can run Granite Code 20B at Q2_K quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 17.5 GB which exceeds available memory, but at Q2_K it needs only 13.1 GB. Expected decode speed: 14.7 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 17.5 GB at Q4_K_M quantization. On RX 6700 XT 12GB, it fits at Q2_K using 13.1 GB.

What is the best quantization for Granite Code 20B?

The recommended quantization is Q4_K_M, but on RX 6700 XT 12GB the best fitting quantization is Q2_K, which uses 13.1 GB.

What speed will Granite Code 20B run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Granite Code 20B achieves approximately 14.7 tokens per second decode speed with a time-to-first-token of 13156ms using Q2_K quantization.

Can RX 6700 XT 12GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on RX 6700 XT 12GB receives a F grade with 6.0 tok/s and 4K context.

What context window can Granite Code 20B use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Granite Code 20B can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on RX 6700 XT 12GB?

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.

See all results for RX 6700 XT 12GB See all hardware for Granite Code 20B

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