Can granite 8b code instruct 4k run on RTX 5000 Ada 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

granite 8b code instruct 4k needs ~10.2 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~94 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 10.2 GB, 94.4 tok/s, Runs well

10.2 GB required32.0 GB available

32% VRAM used

Fit status

Runs well

Decode

94.4 tok/s

TTFT

2050 ms

Safe context

388K

Memory

10.2 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on RTX 5000 Ada 32GB
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: 94.4 tok/s decode · 2.0s TTFT (warm) · 236 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	94.4 tok/s	1118 ms	388K
Coding	C	Runs well	94.4 tok/s	2050 ms	388K
Agentic Coding	C	Runs well	94.4 tok/s	2982 ms	388K
Reasoning	C	Runs well	94.4 tok/s	2423 ms	388K
RAG	C	Runs well	94.4 tok/s	3728 ms	388K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C43
Q3_K_S	3	3.9 GB	Low	C43
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

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.76.8 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run granite 8b code instruct 4k?

Yes, RTX 5000 Ada 32GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 94.4 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 10.2 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, granite 8b code instruct 4k achieves approximately 94.4 tokens per second decode speed with a time-to-first-token of 2050ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on RTX 5000 Ada 32GB receives a C grade with 94.4 tok/s and 388K context.

What context window can granite 8b code instruct 4k use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, granite 8b code instruct 4k can safely use up to 388K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for granite 8b code instruct 4k

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