Can Granite 4.1 8B run on Tesla P100 16GB?

YES — Runs Great

A80Great

Estimated from fit model

Granite 4.1 8B needs ~10.1 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~95 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) — 10.1 GB, 95.1 tok/s, Runs well

10.1 GB required16.0 GB available

63% VRAM used

Fit status

Runs well

Decode

95.1 tok/s

TTFT

2035 ms

Safe context

55K

Memory

10.1 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on Tesla P100 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: 95.1 tok/s decode · 2.0s TTFT (warm) · 238 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	A	Runs well	95.1 tok/s	1110 ms	55K
Coding	A	Runs well	95.1 tok/s	2035 ms	55K
Agentic Coding	A	Runs well	95.1 tok/s	2960 ms	55K
Reasoning	A	Runs well	95.1 tok/s	2405 ms	55K
RAG	A	Runs well	95.1 tok/s	3700 ms	55K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A71
Q3_K_S	3	3.9 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Your hardware

More models your Tesla P100 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	84.6 tok/s
Qwen 3 14B	14B	S	54.6 tok/s

Frequently asked questions

Can Tesla P100 16GB run Granite 4.1 8B?

Yes, Tesla P100 16GB can run Granite 4.1 8B with a A grade (Runs well). Expected decode speed: 95.1 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 8B?

The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 8B run at on Tesla P100 16GB?

On Tesla P100 16GB, Granite 4.1 8B achieves approximately 95.1 tokens per second decode speed with a time-to-first-token of 2035ms using Q4_K_M quantization.

Can Tesla P100 16GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on Tesla P100 16GB receives a A grade with 95.1 tok/s and 55K context.

What context window can Granite 4.1 8B use on Tesla P100 16GB?

On Tesla P100 16GB, Granite 4.1 8B can safely use up to 55K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Tesla P100 16GB See all hardware for Granite 4.1 8B

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