Nemotron 70B needs ~93.7 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With Q8_0 quantization, expect ~3 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
4.2 tok/s
TTFT
46408 ms
Safe context
131K
Memory
61.5 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 4.2 tok/s | 25313 ms | 131K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | B | Runs well | 4.2 tok/s | 67502 ms | 131K |
| Reasoning | B | Runs well | 4.2 tok/s | 54845 ms | 131K |
| RAG | B | Runs well | 4.2 tok/s | 84378 ms | 131K |
How Nemotron 70B (70B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | B64 |
Q3_K_S | 3 | 34.3 GB | Low | B66 |
NVFP4 | 4 | 39.2 GB | Medium | B67 |
Q4_K_M | 4 | 42.7 GB | Medium | B68 |
Q5_K_M | 5 | 50.4 GB | High | B69 |
Q6_K | 6 | 57.4 GB | High | B69 |
Q8_0Best for your GPU | 8 | 74.9 GB | Very High | B69 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Copy-paste commands to run Nemotron 70B on your machine.
Run
ollama run nemotronOpciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 2345%.
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 2345%.
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 3974%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Nemotron 70B at Q8_0 quantization (Tight fit). The recommended Q4_K_M requires 48.5 GB which exceeds available memory, but at Q8_0 it needs only 93.7 GB. Expected decode speed: 2.6 tok/s.
Nemotron 70B (70B parameters) requires approximately 48.5 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at Q8_0 using 93.7 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is Q8_0, which uses 93.7 GB.
On NVIDIA DGX Spark 128GB, Nemotron 70B achieves approximately 2.6 tokens per second decode speed with a time-to-first-token of 74003ms using Q8_0 quantization.
For coding workloads, Nemotron 70B on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Nemotron 70B can safely use up to 65K tokens of context at Q8_0 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.
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