Can granite 8b code instruct 4k run on Radeon Pro W7500 8GB?

Q: What should I upgrade first if granite 8b code instruct 4k feels slow on Radeon Pro W7500 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

C50Usable

Estimated from fit model

granite 8b code instruct 4k needs ~7.5 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 7.5 GB, 27.1 tok/s, Tight fit

7.5 GB required8.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

27.1 tok/s

TTFT

7149 ms

Safe context

24K

Memory

7.5 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on Radeon Pro W7500 8GB
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: 27.1 tok/s decode · 7.1s TTFT (warm) · 68 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	27.1 tok/s	3899 ms	24K
Coding	C	Tight fit	27.1 tok/s	7149 ms	24K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	18.1 tok/s	15576 ms	24K
Reasoning	C	Tight fit	27.1 tok/s	8448 ms	24K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	18.1 tok/s	19470 ms

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4

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 9060 XT 16GBBudget pick

16 GB VRAM (+8)320 GB/s (+96)

Raises estimated decode speed by about 52%.41.3 tok/s decode

Raises estimated decode speed by about 52%.

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

RX 7700 XT 12GBBest value

12 GB VRAM (+4)432 GB/s (+208)

Raises estimated decode speed by about 96%.53.1 tok/s decode

Raises estimated decode speed by about 96%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RX 6700 XT 12GBAMD upgrade

12 GB VRAM (+4)384 GB/s (+160)

Raises estimated decode speed by about 51%.40.9 tok/s decode

Raises estimated decode speed by about 51%.

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run granite 8b code instruct 4k?

Yes, Radeon Pro W7500 8GB can run granite 8b code instruct 4k with a C grade (Tight fit). Expected decode speed: 27.1 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 7.5 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 W7500 8GB?

On Radeon Pro W7500 8GB, granite 8b code instruct 4k achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7149ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Radeon Pro W7500 8GB receives a C grade with 27.1 tok/s and 24K context.

What context window can granite 8b code instruct 4k use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, granite 8b code instruct 4k can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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