Can Granite 4.1 30B run on NVIDIA DGX Spark 128GB?

YES — With F16

A80Great

Estimated from fit model

Granite 4.1 30B needs ~79.7 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~4 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth

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 4.1 30B at Q4_K_M needs 23.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (79.7 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 36.5 GB, 9.6 tok/s, Runs well

36.5 GB required108.8 GB available

34% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20120 ms

Safe context

131K

Memory

36.5 GB / 108.8 GB

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B 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: 9.6 tok/s decode · 20.1s TTFT (warm) · 24 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	9.6 tok/s	10975 ms	131K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	A	Runs well	9.6 tok/s	29266 ms	131K
Reasoning	A	Runs well	9.6 tok/s	23779 ms	131K
RAG	A	Runs well	9.6 tok/s	36582 ms	131K

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A72
Q3_K_S	3	14.7 GB	Low	A73
NVFP4	4	16.8 GB	Medium	A73
Q4_K_M	4	18.3 GB	Medium	A73
Q5_K_M	5	21.6 GB	High	A73
Q6_K	6	24.6 GB	High	A74
Q8_0	8	32.1 GB	Very High	A75
F16Best for your GPU	16	61.5 GB	Maximum	A80

Get started

Copy-paste commands to run Granite 4.1 30B on your machine.

Run

ollama run granite4.1:30b

Opciones de mejora

Hardware que ejecuta bien Granite 4.1 30B

NVIDIA H200 141GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 2368%.236.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2368%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 2368%.236.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2368%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA B200 180GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 4013%.394.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 4013%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Granite 4.1 30B?

Yes, NVIDIA DGX Spark 128GB can run Granite 4.1 30B at F16 quantization (Runs well). The recommended Q4_K_M requires 23.4 GB which exceeds available memory, but at F16 it needs only 79.7 GB. Expected decode speed: 4.0 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 23.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 79.7 GB.

What is the best quantization for Granite 4.1 30B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 79.7 GB.

What speed will Granite 4.1 30B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Granite 4.1 30B achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 48298ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Granite 4.1 30B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Granite 4.1 30B can safely use up to 131K tokens of context at F16 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 4.1 30B feels slow on NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Granite 4.1 30B?

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 4.1 30B

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