Can granite 8b code instruct 4k run on NVIDIA A16 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

granite 8b code instruct 4k needs ~13.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~96 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) — 13.4 GB, 95.9 tok/s, Runs well

13.4 GB required64.0 GB available

21% VRAM used

Fit status

Runs well

Decode

95.9 tok/s

TTFT

2019 ms

Safe context

879K

Memory

13.4 GB / 64.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on NVIDIA A16 64GB
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: 95.9 tok/s decode · 2.0s TTFT (warm) · 240 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	95.9 tok/s	1101 ms	879K
Coding	C	Runs well	95.9 tok/s	2019 ms	879K
Agentic Coding	C	Runs well	95.9 tok/s	2936 ms	879K
Reasoning	C	Runs well	95.9 tok/s	2386 ms	879K
RAG	C	Runs well	95.9 tok/s	3670 ms	879K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C40
Q3_K_S	3	3.9 GB	Low	C40
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 96GBBudget pick

96 GB Unified (+32)

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+219)

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

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run granite 8b code instruct 4k?

Yes, NVIDIA A16 64GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 95.9 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 13.4 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 A16 64GB?

On NVIDIA A16 64GB, granite 8b code instruct 4k achieves approximately 95.9 tokens per second decode speed with a time-to-first-token of 2019ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on NVIDIA A16 64GB receives a C grade with 95.9 tok/s and 879K context.

What context window can granite 8b code instruct 4k use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, granite 8b code instruct 4k can safely use up to 879K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for granite 8b code instruct 4k

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