Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)819 GB/s (+546)
C
Adds memory headroom for longer context windows and future model growth.28 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can Gemma 2 2B run on NVIDIA DGX Spark 128GB?
YES — Runs Great
C46Usable
○Estimated from fit model
Gemma 2 2B needs ~17.1 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 17.1 GB, 28.0 tok/s, Runs well
17.1 GB required108.8 GB available
16% VRAM used
Fit status
Runs well
Decode
28.0 tok/s
TTFT
6914 ms
Safe context
8K
Memory
17.1 GB / 108.8 GB
Memory breakdown
Weights1.2 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsGemma 2 2B 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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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 | 28.0 tok/s | 3771 ms | 8K |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 8K |
| Agentic Coding | C | Runs well | 28.0 tok/s | 10057 ms | 8K |
| Reasoning | F | Too heavy | 24.2 tok/s | 9467 ms | 4K |
| RAG | C | Runs well | 28.0 tok/s | 12571 ms | 8K |
Quantization options
How Gemma 2 2B (2B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | C44 |
Q3_K_S | 3 | 1.0 GB | Low | C44 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Gemma 2 2B on your machine.
Run
lms load gemma-2-2b-it && lms server startUpgrade options