Can Gemma 4 31B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

A81Great

Estimated from fit model

Gemma 4 31B needs ~47.6 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 47.6 GB, 9.2 tok/s, Runs well

47.6 GB required108.8 GB available

44% VRAM used

Fit status

Runs well

Decode

9.2 tok/s

TTFT

21080 ms

Safe context

83K

Memory

47.6 GB / 108.8 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsGemma 4 31B 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: 9.2 tok/s decode · 21.1s TTFT (warm) · 23 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	A	Runs well	9.2 tok/s	11498 ms	83K
Coding	A	Runs well	9.2 tok/s	21080 ms	83K
Agentic Coding	A	Runs well	9.2 tok/s	30662 ms	83K
Reasoning	A	Runs well	9.2 tok/s	24913 ms	83K
RAG	A	Runs well	9.2 tok/s	38327 ms	83K

Inference speed

Gemma 4 31B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 31B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~26 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	25.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	25.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	23.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	19.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	18.7	Fits
RTX 5090 32GB	32 GB	Q4_K_M	15.0	Heavy offload
RTX 4090 24GB	24 GB	Q4_K_M	13.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	13.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	11.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	10.2	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	9.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.8	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	5.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A77
Q3_K_S	3	15.0 GB	Low	A77
NVFP4	4	17.2 GB	Medium	A77
Q4_K_M	4	18.7 GB	Medium	A78
Q5_K_M	5	22.1 GB	High	A78
Q6_K	6	25.2 GB	High	A79
Q8_0	8	32.8 GB	Very High	A80
F16Best for your GPU	16	62.9 GB	Maximum	A85

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your NVIDIA DGX Spark 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen 3.5 122B A10B	122B	S	6.6 tok/s
Qwen 3.6 35B A3B	35B	S	20.8 tok/s
Qwen 3.5 35B A3B	35B	S	22.6 tok/s
Qwen 3 32B	32B	A	9.1 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Gemma 4 31B?

Yes, NVIDIA DGX Spark 128GB can run Gemma 4 31B with a A grade (Runs well). Expected decode speed: 9.2 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 47.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 4 31B achieves approximately 9.2 tokens per second decode speed with a time-to-first-token of 21080ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on NVIDIA DGX Spark 128GB receives a A grade with 9.2 tok/s and 83K context.

What context window can Gemma 4 31B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 4 31B can safely use up to 83K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Gemma 4 31B?

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 4 31B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-4-31b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: