Can Mistral Small 24B run on NVIDIA DGX Spark 128GB?

Q: Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Mistral Small 24B?

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

A74Great

Estimated from fit model

Mistral Small 24B needs ~31.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~12 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) — 31.3 GB, 12.0 tok/s, Runs well

31.3 GB required108.8 GB available

29% VRAM used

Fit status

Runs well

Decode

12.0 tok/s

TTFT

16096 ms

Safe context

33K

Memory

31.3 GB / 108.8 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsMistral Small 24B 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: 12.0 tok/s decode · 16.1s TTFT (warm) · 30 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	12.0 tok/s	8780 ms	33K
Coding	A	Runs well	12.0 tok/s	16096 ms	33K
Agentic Coding	A	Runs well	12.0 tok/s	23413 ms	33K
Reasoning	F	Too heavy	2.0 tok/s	113608 ms	4K
RAG	A	Runs well	12.0 tok/s	29266 ms	33K

Quantization options

How Mistral Small 24B (24B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A72
Q3_K_S	3	11.8 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 24B on your machine.

Run

ollama run mistral-small

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
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Mistral Small 24B?

Yes, NVIDIA DGX Spark 128GB can run Mistral Small 24B with a A grade (Runs well). Expected decode speed: 12.0 tok/s.

How much VRAM does Mistral Small 24B need?

Mistral Small 24B (24B parameters) requires approximately 31.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B?

The recommended quantization for Mistral Small 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mistral Small 24B achieves approximately 12.0 tokens per second decode speed with a time-to-first-token of 16096ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Mistral Small 24B for coding?

For coding workloads, Mistral Small 24B on NVIDIA DGX Spark 128GB receives a A grade with 12.0 tok/s and 33K context.

What context window can Mistral Small 24B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mistral Small 24B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Mistral Small 24B?

See all results for NVIDIA DGX Spark 128GB See all hardware for Mistral Small 24B

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