Can granite 8b code instruct 4k run on Radeon Pro W7900 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

granite 8b code instruct 4k needs ~11.5 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~105 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) — 11.5 GB, 104.5 tok/s, Runs well

11.5 GB required48.0 GB available

24% VRAM used

Fit status

Runs well

Decode

104.5 tok/s

TTFT

1853 ms

Safe context

639K

Memory

11.5 GB / 48.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on Radeon Pro W7900 48GB
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: 104.5 tok/s decode · 1.9s TTFT (warm) · 261 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	104.5 tok/s	1011 ms	639K
Coding	C	Runs well	104.5 tok/s	1853 ms	639K
Agentic Coding	C	Runs well	104.5 tok/s	2696 ms	639K
Reasoning	C	Runs well	104.5 tok/s	2190 ms	639K
RAG	C	Runs well	104.5 tok/s	3370 ms	639K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C41
Q3_K_S	3	3.9 GB	Low	C41
NVFP4	4	4.5 GB	Medium	C42
Q4_K_M	4	4.9 GB	Medium	C42
Q5_K_M	5	5.8 GB	High	C42
Q6_K	6	6.6 GB	High	C42
Q8_0	8	8.6 GB	Very High	C42
F16Best for your GPU	16	16.4 GB	Maximum	C45

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

Opciones de mejora

Hardware que ejecuta bien granite 8b code instruct 4k

Mac Studio M3 Ultra 96GBOpción económica

96 GB Unified (+48)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.112 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run granite 8b code instruct 4k?

Yes, Radeon Pro W7900 48GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 104.5 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 11.5 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 Pro W7900 48GB?

On Radeon Pro W7900 48GB, granite 8b code instruct 4k achieves approximately 104.5 tokens per second decode speed with a time-to-first-token of 1853ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Radeon Pro W7900 48GB receives a C grade with 104.5 tok/s and 639K context.

What context window can granite 8b code instruct 4k use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, granite 8b code instruct 4k can safely use up to 639K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for granite 8b code instruct 4k

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