Can Granite 3.1 8B run on Tesla P40 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

Granite 3.1 8B needs ~10.1 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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, 51.7 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

51.7 tok/s

TTFT

3744 ms

Safe context

128K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on Tesla P40 24GB
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: 51.7 tok/s decode · 3.7s TTFT (warm) · 129 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	51.7 tok/s	2042 ms	128K
Coding	C	Runs well	51.7 tok/s	3744 ms	128K
Agentic Coding	B	Runs well	51.7 tok/s	5445 ms	128K
Reasoning	C	Runs well	51.7 tok/s	4424 ms	128K
RAG	B	Runs well	51.7 tok/s	6807 ms	128K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C49
NVFP4	4	4.5 GB	Medium	C50
Q4_K_M	4	4.9 GB	Medium	C50
Q5_K_M	5	5.8 GB	High	C51
Q6_K	6	6.6 GB	High	C51
Q8_0	8	8.6 GB	Very High	C52
F16Best for your GPU	16	16.4 GB	Maximum	C54

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Opções de upgrade

Hardware que roda bem Granite 3.1 8B

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 194%.152 tok/s decodificação

Raises estimated decode speed by about 194%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 117%.112 tok/s decodificação

Raises estimated decode speed by about 117%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 117%.112 tok/s decodificação

Raises estimated decode speed by about 117%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Granite 3.1 8B?

Yes, Tesla P40 24GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 51.7 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on Tesla P40 24GB?

On Tesla P40 24GB, Granite 3.1 8B achieves approximately 51.7 tokens per second decode speed with a time-to-first-token of 3744ms using Q4_K_M quantization.

Can Tesla P40 24GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Tesla P40 24GB receives a C grade with 51.7 tok/s and 128K context.

What context window can Granite 3.1 8B use on Tesla P40 24GB?

On Tesla P40 24GB, Granite 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Granite 3.1 8B

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