Can granite 8b code instruct 4k run on NVIDIA T4 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

granite 8b code instruct 4k needs ~8.6 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.6 GB, 42.6 tok/s, Runs well

8.6 GB required16.0 GB available

54% VRAM used

Fit status

Runs well

Decode

42.6 tok/s

TTFT

4542 ms

Safe context

142K

Memory

8.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on NVIDIA T4 16GB
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: 42.6 tok/s decode · 4.5s TTFT (warm) · 107 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	42.6 tok/s	2478 ms	142K
Coding	C	Runs well	42.6 tok/s	4542 ms	142K
Agentic Coding	C	Runs well	42.6 tok/s	6607 ms	142K
Reasoning	C	Runs well	42.6 tok/s	5368 ms	142K
RAG	C	Runs well	42.6 tok/s	8258 ms	142K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
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

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 131%.98.4 tok/s decode

Raises estimated decode speed by about 131%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 140%.102.3 tok/s decode

Raises estimated decode speed by about 140%.

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

~$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run granite 8b code instruct 4k?

Yes, NVIDIA T4 16GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 42.6 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 8.6 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 T4 16GB?

On NVIDIA T4 16GB, granite 8b code instruct 4k achieves approximately 42.6 tokens per second decode speed with a time-to-first-token of 4542ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on NVIDIA T4 16GB receives a C grade with 42.6 tok/s and 142K context.

What context window can granite 8b code instruct 4k use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, granite 8b code instruct 4k can safely use up to 142K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for granite 8b code instruct 4k

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