Can granite 8b code instruct 4k run on NVIDIA B200 180GB?

YES — Runs Great

C45Usable

Estimated from fit model

granite 8b code instruct 4k needs ~25.0 GB VRAM. NVIDIA B200 180GB has 180.0 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) — 25.0 GB, 112.0 tok/s, Runs well

25.0 GB required180.0 GB available

14% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

2.7M

Memory

25.0 GB / 180.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on NVIDIA B200 180GB
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	2.7M
Coding	C	Runs well	112.0 tok/s	1729 ms	2.7M
Agentic Coding	C	Runs well	112.0 tok/s	2514 ms	2.7M
Reasoning	C	Runs well	112.0 tok/s	2043 ms	2.7M
RAG	C	Runs well	112.0 tok/s	3143 ms	2.7M

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	D37
Q3_K_S	3	3.9 GB	Low	D37
NVFP4	4	4.5 GB	Medium	D37
Q4_K_M	4	4.9 GB	Medium	D37
Q5_K_M	5	5.8 GB	High	D37
Q6_K	6	6.6 GB	High	D37
Q8_0	8	8.6 GB	Very High	D37
F16Best for your GPU	16	16.4 GB	Maximum	D37

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

Mac Studio M3 Ultra 256GBGünstige Wahl

256 GB Unified (+76)

This setup is broadly balanced for this model.112 tok/s Dekodierung

ca. $6,999 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run granite 8b code instruct 4k?

Yes, NVIDIA B200 180GB 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 25.0 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 B200 180GB?

On NVIDIA B200 180GB, 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 B200 180GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on NVIDIA B200 180GB receives a C grade with 112.0 tok/s and 2.7M context.

What context window can granite 8b code instruct 4k use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, granite 8b code instruct 4k can safely use up to 2.7M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for granite 8b code instruct 4k

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