Can Gemma 3 27B run on NVIDIA DGX Spark 128GB?

YES — With F16

A80Great

Estimated from fit model

Gemma 3 27B needs ~80.8 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.

Gemma 3 27B at Q4_K_M needs 28.9 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (80.8 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 42.0 GB, 10.4 tok/s, Runs well

42.0 GB required108.8 GB available

39% VRAM used

Fit status

Runs well

Decode

10.4 tok/s

TTFT

18539 ms

Safe context

111K

Memory

42.0 GB / 108.8 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsGemma 3 27B 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: 10.4 tok/s decode · 18.5s TTFT (warm) · 26 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	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	A	Runs well	10.4 tok/s	26966 ms	111K
Reasoning	A	Runs well	10.4 tok/s	21910 ms	111K
RAG	A	Runs well	10.4 tok/s	33708 ms	111K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A72
Q3_K_S	3	13.2 GB	Low	A72
NVFP4	4	15.1 GB	Medium	A73
Q4_K_M	4	16.5 GB	Medium	A73
Q5_K_M	5	19.4 GB	High	A73
Q6_K	6	22.1 GB	High	A74
Q8_0	8	28.9 GB	Very High	A75
F16Best for your GPU	16	55.4 GB	Maximum	A80

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Upgrade-Optionen

Hardware, die Gemma 3 27B gut ausführt

NVIDIA H200 141GBGünstige Wahl

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

Raises estimated decode speed by about 2371%.257 tok/s Dekodierung

Raises estimated decode speed by about 2371%.

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 2371%.257 tok/s Dekodierung

Raises estimated decode speed by about 2371%.

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 3535%.378 tok/s Dekodierung

Raises estimated decode speed by about 3535%.

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

ca. $30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Gemma 3 27B?

Yes, NVIDIA DGX Spark 128GB can run Gemma 3 27B at F16 quantization (Runs well). The recommended Q4_K_M requires 28.9 GB which exceeds available memory, but at F16 it needs only 80.8 GB. Expected decode speed: 4.4 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 28.9 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 80.8 GB.

What is the best quantization for Gemma 3 27B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 80.8 GB.

What speed will Gemma 3 27B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 3 27B achieves approximately 4.4 tokens per second decode speed with a time-to-first-token of 44503ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Gemma 3 27B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 3 27B can safely use up to 56K 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 Gemma 3 27B 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 Gemma 3 27B?

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 Gemma 3 27B

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