Can granite 8b code instruct 4k run on Radeon RX 7900M 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

granite 8b code instruct 4k needs ~8.3 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.3 GB, 69.6 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

69.6 tok/s

TTFT

2780 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on Radeon RX 7900M 16GB
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: 69.6 tok/s decode · 2.8s TTFT (warm) · 174 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	69.6 tok/s	1516 ms	147K
Coding	C	Runs well	69.6 tok/s	2780 ms	147K
Agentic Coding	C	Runs well	69.6 tok/s	4044 ms	147K
Reasoning	C	Runs well	69.6 tok/s	3285 ms	147K
RAG	C	Runs well	69.6 tok/s	5055 ms	147K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C49
Q6_K	6	6.6 GB	High	C50
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
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 Radeon RX 7900M 16GB run granite 8b code instruct 4k?

Yes, Radeon RX 7900M 16GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 69.6 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 8.3 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 Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, granite 8b code instruct 4k achieves approximately 69.6 tokens per second decode speed with a time-to-first-token of 2780ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Radeon RX 7900M 16GB receives a C grade with 69.6 tok/s and 147K context.

What context window can granite 8b code instruct 4k use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, granite 8b code instruct 4k can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for granite 8b code instruct 4k

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