Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Llama 3.2 3B Instruct?

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 3B Instruct run on NVIDIA DGX Spark 128GB?

YES — With F16

C43Usable

Estimated from fit model

Llama 3.2 3B Instruct needs ~20.8 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~37 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 3B Instruct at Q5_K_M needs 3.7 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (20.8 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 16.8 GB, 42.0 tok/s, Runs well

16.8 GB required108.8 GB available

15% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

4.2M

Memory

16.8 GB / 108.8 GB

Memory breakdown

Weights2.2 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsLlama 3.2 3B Instruct 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2514 ms	4.2M
Coding	F	Too heavy	13.9 tok/s	13905 ms	4K
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	4.2M
Reasoning	C	Runs well	42.0 tok/s	5448 ms	4.2M
RAG	C	Runs well	42.0 tok/s	8381 ms	4.2M

Quantization options

How Llama 3.2 3B Instruct (3B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	D40
Q3_K_S	3	1.5 GB	Low	D40
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 3B Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bartowski/Llama-3.2-3B-Instruct-GGUF" \
  --hf-file "Llama-3.2-3B-Instruct-GGUF-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Llama 3.2 3B Instruct well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+546)

Adds memory headroom for longer context windows and future model growth.42 tok/s decode

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

~$6,999 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Llama 3.2 3B Instruct?

Yes, NVIDIA DGX Spark 128GB can run Llama 3.2 3B Instruct at F16 quantization (Runs well). The recommended Q5_K_M requires 3.7 GB which exceeds available memory, but at F16 it needs only 20.8 GB. Expected decode speed: 37.3 tok/s.

How much VRAM does Llama 3.2 3B Instruct need?

Llama 3.2 3B Instruct (3B parameters) requires approximately 3.7 GB at Q5_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 20.8 GB.

What is the best quantization for Llama 3.2 3B Instruct?

The recommended quantization is Q5_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 20.8 GB.

What speed will Llama 3.2 3B Instruct run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 3.2 3B Instruct achieves approximately 37.3 tokens per second decode speed with a time-to-first-token of 5192ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Llama 3.2 3B Instruct for coding?

For coding workloads, Llama 3.2 3B Instruct on NVIDIA DGX Spark 128GB receives a F grade with 13.9 tok/s and 4K context.

What context window can Llama 3.2 3B Instruct use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 3.2 3B Instruct can safely use up to 4.0M tokens of context at F16 quantization. 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 Llama 3.2 3B Instruct

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