Can Gemma 2 9B run on NVIDIA DGX Spark 128GB?

Q: Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Gemma 2 9B?

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.

YES — Runs Great

B58Good

Estimated from fit model

Gemma 2 9B needs ~24.9 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~31 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) — 24.9 GB, 31.3 tok/s, Runs well

24.9 GB required108.8 GB available

23% VRAM used

Fit status

Runs well

Decode

31.3 tok/s

TTFT

6180 ms

Safe context

Memory

24.9 GB / 108.8 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsGemma 2 9B 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: 31.3 tok/s decode · 6.2s TTFT (warm) · 78 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	31.3 tok/s	3371 ms	8K
Coding	B	Runs well	31.3 tok/s	6180 ms	8K
Agentic Coding	B	Runs well	31.3 tok/s	8989 ms	8K
Reasoning	B	Runs well	31.3 tok/s	7303 ms	8K
RAG	B	Runs well	31.3 tok/s	11236 ms	8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C54
Q3_K_S	3	4.4 GB	Low	C54
NVFP4	4

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 303%.126 tok/s decode

Raises estimated decode speed by about 303%.

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 303%.126 tok/s decode

Raises estimated decode speed by about 303%.

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 303%.126 tok/s decode

Raises estimated decode speed by about 303%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Gemma 2 9B?

Yes, NVIDIA DGX Spark 128GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 31.3 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 2 9B achieves approximately 31.3 tokens per second decode speed with a time-to-first-token of 6180ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on NVIDIA DGX Spark 128GB receives a B grade with 31.3 tok/s and 8K context.

What context window can Gemma 2 9B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Gemma 2 9B?

See all results for NVIDIA DGX Spark 128GB See all hardware for Gemma 2 9B

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