Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Llama 3.1 8B?

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 3.1 8B run on NVIDIA DGX Spark 128GB?

YES — With F16

B64Good

Estimated from fit model

Llama 3.1 8B needs ~32.6 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~15 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 3.1 8B at Q4_K_M needs 8.0 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (32.6 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 21.1 GB, 36.1 tok/s, Runs well

21.1 GB required108.8 GB available

19% VRAM used

Fit status

Runs well

Decode

36.1 tok/s

TTFT

5365 ms

Safe context

128K

Memory

21.1 GB / 108.8 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B 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: 36.1 tok/s decode · 5.4s TTFT (warm) · 90 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	36.1 tok/s	2927 ms	128K
Coding	F	Too heavy	6.0 tok/s	32043 ms	4K
Agentic Coding	B	Runs well	36.1 tok/s	7804 ms	128K
Reasoning	B	Runs well	36.1 tok/s	6341 ms	128K
RAG	B	Runs well	36.1 tok/s	9755 ms	128K

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B60
Q3_K_S	3	3.9 GB	Low	B60
NVFP4	4

Get started

Copy-paste commands to run Llama 3.1 8B on your machine.

Run

ollama run llama3.1

Upgrade options

Hardware that runs Llama 3.1 8B well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 210%.112 tok/s decode

Raises estimated decode speed by about 210%.

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 210%.112 tok/s decode

Raises estimated decode speed by about 210%.

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 210%.112 tok/s decode

Raises estimated decode speed by about 210%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Llama 3.1 8B?

Yes, NVIDIA DGX Spark 128GB can run Llama 3.1 8B at F16 quantization (Runs well). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at F16 it needs only 32.6 GB. Expected decode speed: 15.0 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 32.6 GB.

What is the best quantization for Llama 3.1 8B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 32.6 GB.

What speed will Llama 3.1 8B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 3.1 8B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12879ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on NVIDIA DGX Spark 128GB receives a F grade with 6.0 tok/s and 4K context.

What context window can Llama 3.1 8B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Llama 3.1 8B can safely use up to 128K tokens of context at F16 quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA DGX Spark 128GB See all hardware for Llama 3.1 8B

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