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

YES — With F16

A78Great

Estimated from fit model

Mistral Small 24B needs ~65.9 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~5 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.

Mistral Small 24B at Q4_K_M needs 18.3 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (65.9 GB) with maximum quality. 8 quantization levels fit.

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

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	52435 ms	4K
Coding	F	Too heavy	2.0 tok/s	96130 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	139826 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	113608 ms	4K
RAG	F	Too heavy	2.0 tok/s	174782 ms	4K

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	13.4 GB	Medium	A72
Q4_K_M	4	14.6 GB	Medium	A72
Q5_K_M	5	17.3 GB	High	A72
Q6_K	6	19.7 GB	High	A73
Q8_0	8	25.7 GB	Very High	A74
F16Best for your GPU	16	49.2 GB	Maximum	A79

Get started

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

Run

ollama run mistral-small

アップグレードオプション

Mistral Small 24Bを快適に動かすハードウェア

NVIDIA H200 141GBコスパ重視

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

Raises estimated decode speed by about 2368%.296.1 tok/sデコード

Raises estimated decode speed by about 2368%.

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

〜$30,000 MSRP

NVIDIA H200 PCIe 141GBコスパ最良

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

Raises estimated decode speed by about 2368%.296.1 tok/sデコード

Raises estimated decode speed by about 2368%.

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

〜$30,000 MSRP

NVIDIA B200 180GBNVIDIAアップグレード

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

Raises estimated decode speed by about 2700%.336 tok/sデコード

Raises estimated decode speed by about 2700%.

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

〜$30,000 MSRP

Frequently asked questions

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

Yes, NVIDIA DGX Spark 128GB can run Mistral Small 24B at F16 quantization (Runs well). The recommended Q4_K_M requires 18.3 GB which exceeds available memory, but at F16 it needs only 65.9 GB. Expected decode speed: 5.0 tok/s.

How much VRAM does Mistral Small 24B need?

Mistral Small 24B (24B parameters) requires approximately 18.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 65.9 GB.

What is the best quantization for Mistral Small 24B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 65.9 GB.

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

On NVIDIA DGX Spark 128GB, Mistral Small 24B achieves approximately 5.0 tokens per second decode speed with a time-to-first-token of 38638ms using F16 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 F grade with 2.0 tok/s and 4K 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 at F16 quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 24B 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 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.

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

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