Can granite 8b code instruct 4k run on NVIDIA H100 80GB?

YES — Runs Great

C46Usable

Estimated from fit model

granite 8b code instruct 4k needs ~15.0 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 15.0 GB, 112.0 tok/s, Runs well

15.0 GB required80.0 GB available

19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

1.1M

Memory

15.0 GB / 80.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on NVIDIA H100 80GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	112.0 tok/s	943 ms	1.1M
Coding	C	Runs well	112.0 tok/s	1729 ms	1.1M
Agentic Coding	C	Runs well	112.0 tok/s	2514 ms	1.1M
Reasoning	C	Runs well	112.0 tok/s	2043 ms	1.1M
RAG	C	Runs well	112.0 tok/s	3143 ms	1.1M

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	D40
Q3_K_S	3	3.9 GB	Low	D40
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

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.95.1 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.90.2 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run granite 8b code instruct 4k?

Yes, NVIDIA H100 80GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 15.0 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 NVIDIA H100 80GB?

On NVIDIA H100 80GB, granite 8b code instruct 4k achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on NVIDIA H100 80GB receives a C grade with 112.0 tok/s and 1.1M context.

What context window can granite 8b code instruct 4k use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, granite 8b code instruct 4k can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for granite 8b code instruct 4k

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