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.
Operating mode
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
Fit status
Runs well
Decode
31.3 tok/s
TTFT
6180 ms
Safe context
8K
Memory
24.9 GB / 108.8 GB
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.
| 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 |
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 | 5.0 GB | Medium | C54 |
Q4_K_M | 4 | 5.5 GB | Medium | C54 |
Q5_K_M | 5 | 6.5 GB | High | C54 |
Q6_K | 6 | 7.4 GB | High | C54 |
Q8_0 | 8 | 9.6 GB | Very High | C54 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B55 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Opciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 303%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 303%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA B200 180GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 303%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 31.3 tok/s.
Gemma 2 9B (9B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
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.
For coding workloads, Gemma 2 9B on NVIDIA DGX Spark 128GB receives a B grade with 31.3 tok/s and 8K context.
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.
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.
Paste this snippet into any page to show a live fit card.
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