Can Granite 3.1 8B run on NVIDIA DGX Spark 128GB?

YES — With F16

C47Usable

Estimated from fit model

Granite 3.1 8B needs ~32.3 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~17 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.

Granite 3.1 8B at Q4_K_M needs 7.7 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (32.3 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.8 GB, 41.5 tok/s, Runs well

20.8 GB required108.8 GB available

19% VRAM used

Fit status

Runs well

Decode

41.5 tok/s

TTFT

4666 ms

Safe context

128K

Memory

20.8 GB / 108.8 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on NVIDIA DGX Spark 128GB
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: 41.5 tok/s decode · 4.7s TTFT (warm) · 104 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	41.5 tok/s	2545 ms	128K
Coding	F	Too heavy	6.9 tok/s	27864 ms	4K
Agentic Coding	C	Runs well	41.5 tok/s	6786 ms	128K
Reasoning	C	Runs well	41.5 tok/s	5514 ms	128K
RAG	C	Runs well	41.5 tok/s	8483 ms	128K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C44
Q3_K_S	3	3.9 GB	Low	C44
NVFP4	4	4.5 GB	Medium	C44
Q4_K_M	4	4.9 GB	Medium	C44
Q5_K_M	5	5.8 GB	High	C44
Q6_K	6	6.6 GB	High	C44
Q8_0	8	8.6 GB	Very High	C44
F16Best for your GPU	16	16.4 GB	Maximum	C45

Get started

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

Run

ollama run granite3.1-dense

Upgrade-Optionen

Hardware, die Granite 3.1 8B gut ausführt

NVIDIA H200 141GBGünstige Wahl

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 170%.112 tok/s Dekodierung

Raises estimated decode speed by about 170%.

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

ca. $30,000 MSRP

NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-Verhältnis

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 170%.112 tok/s Dekodierung

Raises estimated decode speed by about 170%.

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

ca. $30,000 MSRP

NVIDIA B200 180GBNVIDIA-Upgrade

180 GB VRAM (+52)8000 GB/s (+7727)

Raises estimated decode speed by about 170%.112 tok/s Dekodierung

Raises estimated decode speed by about 170%.

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

ca. $30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Granite 3.1 8B?

Yes, NVIDIA DGX Spark 128GB can run Granite 3.1 8B at F16 quantization (Runs well). The recommended Q4_K_M requires 7.7 GB which exceeds available memory, but at F16 it needs only 32.3 GB. Expected decode speed: 17.3 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 7.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 32.3 GB.

What is the best quantization for Granite 3.1 8B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 32.3 GB.

What speed will Granite 3.1 8B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Granite 3.1 8B achieves approximately 17.3 tokens per second decode speed with a time-to-first-token of 11200ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on NVIDIA DGX Spark 128GB receives a F grade with 6.9 tok/s and 4K context.

What context window can Granite 3.1 8B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Granite 3.1 8B can safely use up to 128K tokens of context at F16 quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Granite 3.1 8B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for Granite 3.1 8B

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