Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Llama 3.2 11B Vision?

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 Llama 3.2 11B Vision run on NVIDIA DGX Spark 128GB?

YES — With F16

B57Good

Estimated from fit model

Llama 3.2 11B Vision needs ~38.8 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 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.

Llama 3.2 11B Vision at Q4_K_M needs 9.9 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (38.8 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.9 GB, 26.2 tok/s, Runs well

22.9 GB required108.8 GB available

21% VRAM used

Fit status

Runs well

Decode

26.2 tok/s

TTFT

7377 ms

Safe context

16K

Memory

22.9 GB / 108.8 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision 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: 26.2 tok/s decode · 7.4s TTFT (warm) · 66 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	B	Runs well	26.2 tok/s	4024 ms	16K
Coding	F	Too heavy	4.4 tok/s	44060 ms	4K
Agentic Coding	B	Runs well	26.2 tok/s	10731 ms	16K
Reasoning	B	Runs well	26.2 tok/s	8719 ms	16K
RAG	B	Runs well	26.2 tok/s	13414 ms	16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	C54
Q3_K_S	3	5.4 GB	Low	C54
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade options

Hardware that runs Llama 3.2 11B Vision well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 488%.154 tok/s decode

Raises estimated decode speed by about 488%.

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 488%.154 tok/s decode

Raises estimated decode speed by about 488%.

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 488%.154 tok/s decode

Raises estimated decode speed by about 488%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Llama 3.2 11B Vision?

Yes, NVIDIA DGX Spark 128GB can run Llama 3.2 11B Vision at F16 quantization (Runs well). The recommended Q4_K_M requires 9.9 GB which exceeds available memory, but at F16 it needs only 38.8 GB. Expected decode speed: 10.9 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 9.9 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 38.8 GB.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 38.8 GB.

What speed will Llama 3.2 11B Vision run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 3.2 11B Vision achieves approximately 10.9 tokens per second decode speed with a time-to-first-token of 17709ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on NVIDIA DGX Spark 128GB receives a F grade with 4.4 tok/s and 4K context.

What context window can Llama 3.2 11B Vision use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context at F16 quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA DGX Spark 128GB See all hardware for Llama 3.2 11B Vision

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