Will It Run AI

Can Gemma 3 4B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

B65Good
Estimated from fit model

Gemma 3 4B needs ~18.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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) 18.8 GB, 56.0 tok/s, Runs well
18.8 GB required108.8 GB available
17% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

128K

Memory

18.8 GB / 108.8 GB

Memory breakdown

Weights2.4 GB
KV Cache2.1 GB
Runtime1.2 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelsGemma 3 4B on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well56.0 tok/s1886 ms128K
CodingBRuns well56.0 tok/s3457 ms128K
Agentic CodingBRuns well56.0 tok/s5029 ms128K
ReasoningBRuns well56.0 tok/s4086 ms128K
RAGBRuns well56.0 tok/s6286 ms128K

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.6 GB
LowB60
Q3_K_S
3
2.0 GB
LowB60
NVFP4
4
2.2 GB
MediumB60
Q4_K_M
4
2.4 GB
MediumB60
Q5_K_M
5
2.9 GB
HighB60
Q6_K
6
3.3 GB
HighB60
Q8_0
8
4.3 GB
Very HighB60
F16Best for your GPU
16
8.2 GB
MaximumB61

Get started

Copy-paste commands to run Gemma 3 4B on your machine.

Run

ollama run gemma3:4b

升级选项

能流畅运行 Gemma 3 4B 的硬件

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Gemma 3 4B?

Yes, NVIDIA DGX Spark 128GB can run Gemma 3 4B with a B grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 18.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on NVIDIA DGX Spark 128GB receives a B grade with 56.0 tok/s and 128K context.

What context window can Gemma 3 4B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Gemma 3 4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Gemma 3 4B?

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.

See all results for NVIDIA DGX Spark 128GBSee all hardware for Gemma 3 4B
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