Can granite 8b code instruct 4k run on RX 7900 XT 20GB?

YES — Runs Great

C51Usable

Estimated from fit model

granite 8b code instruct 4k needs ~8.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 8.7 GB, 98.4 tok/s, Runs well

8.7 GB required20.0 GB available

43% VRAM used

Fit status

Runs well

Decode

98.4 tok/s

TTFT

1968 ms

Safe context

209K

Memory

8.7 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on RX 7900 XT 20GB
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: 98.4 tok/s decode · 2.0s TTFT (warm) · 246 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	98.4 tok/s	1074 ms	209K
Coding	C	Runs well	98.4 tok/s	1968 ms	209K
Agentic Coding	C	Runs well	98.4 tok/s	2863 ms	209K
Reasoning	C	Runs well	98.4 tok/s	2326 ms	209K
RAG	C	Runs well	98.4 tok/s	3579 ms	209K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C47
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C48
Q8_0Best for your GPU	8	8.6 GB	Very High	C49
F16	16	16.4 GB	Maximum	F0

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

Frequently asked questions

Can RX 7900 XT 20GB run granite 8b code instruct 4k?

Yes, RX 7900 XT 20GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 98.4 tok/s.

How much VRAM does granite 8b code instruct 4k need?

granite 8b code instruct 4k (8B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for granite 8b code instruct 4k?

The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.

What speed will granite 8b code instruct 4k run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, granite 8b code instruct 4k achieves approximately 98.4 tokens per second decode speed with a time-to-first-token of 1968ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on RX 7900 XT 20GB receives a C grade with 98.4 tok/s and 209K context.

What context window can granite 8b code instruct 4k use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, granite 8b code instruct 4k can safely use up to 209K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for granite 8b code instruct 4k

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