Can Mistral Small 3.2 24B Instruct 2506 run on NVIDIA DGX Spark 128GB?

YES — With F16

C46Usable

Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~66.3 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 3.2 24B Instruct 2506 at Q4_K_M needs 18.7 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (66.3 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.7 GB, 11.2 tok/s, Runs well

31.7 GB required108.8 GB available

29% VRAM used

Fit status

Runs well

Decode

11.2 tok/s

TTFT

17303 ms

Safe context

455K

Memory

31.7 GB / 108.8 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.2 tok/s decode · 17.3s TTFT (warm) · 28 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 3.2 24B Instruct 2506 (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	D40
Q3_K_S	3	11.8 GB	Low	D40
NVFP4	4	13.4 GB	Medium	C40
Q4_K_M	4	14.6 GB	Medium	C40
Q5_K_M	5	17.3 GB	High	C41
Q6_K	6	19.7 GB	High	C41
Q8_0	8	25.7 GB	Very High	C42
F16Best for your GPU	16	49.2 GB	Maximum	C47

Get started

Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.

Run

lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Mistral Small 3.2 24B Instruct 2506

NVIDIA H200 141GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 2359%.275.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2359%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 2359%.275.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2359%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA B200 180GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 2900%.336 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2900%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Mistral Small 3.2 24B Instruct 2506?

Yes, NVIDIA DGX Spark 128GB can run Mistral Small 3.2 24B Instruct 2506 at F16 quantization (Runs well). The recommended Q4_K_M requires 18.7 GB which exceeds available memory, but at F16 it needs only 66.3 GB. Expected decode speed: 4.7 tok/s.

How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?

Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 18.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 66.3 GB.

What is the best quantization for Mistral Small 3.2 24B Instruct 2506?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 66.3 GB.

What speed will Mistral Small 3.2 24B Instruct 2506 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41536ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 258K tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 3.2 24B Instruct 2506 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 3.2 24B Instruct 2506?

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 3.2 24B Instruct 2506

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