Can Qwen 2.5 32B run on NVIDIA DGX Spark 128GB?

Q: Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen 2.5 32B?

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.

YES — Runs Great

A76Great

Estimated from fit model

Qwen 2.5 32B needs ~37.7 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) — 37.7 GB, 9.1 tok/s, Runs well

37.7 GB required108.8 GB available

35% VRAM used

Fit status

Runs well

Decode

9.1 tok/s

TTFT

21362 ms

Safe context

131K

Memory

37.7 GB / 108.8 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B 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: 9.1 tok/s decode · 21.4s 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	9.1 tok/s	11652 ms	131K
Coding	A	Runs well	9.1 tok/s	21362 ms	131K
Agentic Coding	A	Runs well	9.1 tok/s	31072 ms	131K
Reasoning	A	Runs well	9.1 tok/s	25246 ms	131K
RAG	A	Runs well	9.1 tok/s	38841 ms	131K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A73
Q3_K_S	3	15.7 GB	Low	A74
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 32B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your NVIDIA DGX Spark 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Qwen 2.5 32B?

Yes, NVIDIA DGX Spark 128GB can run Qwen 2.5 32B with a A grade (Runs well). Expected decode speed: 9.1 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 37.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization for Qwen 2.5 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 32B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 2.5 32B achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21362ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on NVIDIA DGX Spark 128GB receives a A grade with 9.1 tok/s and 131K context.

What context window can Qwen 2.5 32B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Qwen 2.5 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen 2.5 32B?

See all results for NVIDIA DGX Spark 128GB See all hardware for Qwen 2.5 32B

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