Can granite 8b code instruct 4k run on Radeon PRO W7600 8GB?

YES — Tight Fit

C51Usable

Estimated from fit model

granite 8b code instruct 4k needs ~7.5 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 7.5 GB, 34.8 tok/s, Tight fit

7.5 GB required8.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

34.8 tok/s

TTFT

5560 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 W7600 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: 34.8 tok/s decode · 5.6s TTFT (warm) · 87 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	34.8 tok/s	3033 ms	24K
Coding	C	Tight fit	34.8 tok/s	5560 ms	24K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	23.2 tok/s	12115 ms	24K
Reasoning	C	Tight fit	34.8 tok/s	6571 ms	24K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	23.2 tok/s	15144 ms	24K

Inference speed

granite 8b code instruct 4k inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for granite 8b code instruct 4k at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	95.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	90.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	77.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	76.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	76.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	49.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	48.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	45.1	Fits
RTX 4060 8GB	8 GB	Q4_K_M	40.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	39.6	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Radeon PRO W7600 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	4.5 GB	Medium	C53
Q4_K_MBest for your GPU	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
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

Upgrade-Optionen

Hardware, die granite 8b code instruct 4k gut ausführt

RX 7700 XT 12GBGünstige Wahl

12 GB VRAM (+4)432 GB/s (+144)

Raises estimated decode speed by about 53%.53.1 tok/s Dekodierung

Raises estimated decode speed by about 53%.

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

ca. $449 MSRP

RX 6700 XT 12GBBestes Preis-Leistungs-Verhältnis

12 GB VRAM (+4)384 GB/s (+96)

Adds memory headroom for longer context windows and future model growth.40.9 tok/s Dekodierung

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

ca. $479 MSRP

RX 9070 16GBAMD-Upgrade

16 GB VRAM (+8)640 GB/s (+352)

Raises estimated decode speed by about 134%.81.3 tok/s Dekodierung

Raises estimated decode speed by about 134%.

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

ca. $479 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run granite 8b code instruct 4k?

Yes, Radeon PRO W7600 8GB can run granite 8b code instruct 4k with a C grade (Tight fit). Expected decode speed: 34.8 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 W7600 8GB?

On Radeon PRO W7600 8GB, granite 8b code instruct 4k achieves approximately 34.8 tokens per second decode speed with a time-to-first-token of 5560ms using Q4_K_M quantization.

Can Radeon PRO W7600 8GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Radeon PRO W7600 8GB receives a C grade with 34.8 tok/s and 24K context.

What context window can granite 8b code instruct 4k use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 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.

What should I upgrade first if granite 8b code instruct 4k feels slow on Radeon PRO W7600 8GB?

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

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