Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Mistral Small 24B Instruct 2501?

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.

Can Mistral Small 24B Instruct 2501 run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C41Usable

Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~31.7 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~11 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) — 31.7 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 24B Instruct 2501 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

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	C	Runs well	11.2 tok/s	9438 ms	455K
Coding	C	Runs well	11.2 tok/s	17303 ms	455K
Agentic Coding	C	Runs well	11.2 tok/s	25169 ms	455K
Reasoning	C	Runs well	11.2 tok/s	20450 ms	455K
RAG	C	Runs well	11.2 tok/s	31461 ms	455K

Quantization options

How Mistral Small 24B Instruct 2501 (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

Get started

Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server start

Upgrade options

Hardware that runs Mistral Small 24B Instruct 2501 well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 2359%.275.4 tok/s decode

Raises estimated decode speed by about 2359%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBBest value

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

Raises estimated decode speed by about 2359%.275.4 tok/s decode

Raises estimated decode speed by about 2359%.

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

~$30,000 MSRP

NVIDIA B200 180GBNVIDIA upgrade

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

Raises estimated decode speed by about 2900%.336 tok/s decode

Raises estimated decode speed by about 2900%.

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 Instruct 2501?

Yes, NVIDIA DGX Spark 128GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 11.2 tok/s.

How much VRAM does Mistral Small 24B Instruct 2501 need?

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 31.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B Instruct 2501?

The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B Instruct 2501 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mistral Small 24B Instruct 2501 achieves approximately 11.2 tokens per second decode speed with a time-to-first-token of 17303ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on NVIDIA DGX Spark 128GB receives a C grade with 11.2 tok/s and 455K context.

What context window can Mistral Small 24B Instruct 2501 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mistral Small 24B Instruct 2501 can safely use up to 455K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA DGX Spark 128GB See all hardware for Mistral Small 24B Instruct 2501

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