Nemotron 70B needs ~36.3 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q2_K quantization, expect ~14 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
19.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.9 tok/s
TTFT
32598 ms
Safe context
4K
Memory
51.7 GB / 32.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 3.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.4 tok/s | 16416 ms | 4K |
| Coding | F | Too heavy | 5.5 tok/s | 35450 ms | 4K |
| Agentic Coding | F | Too heavy | 5.1 tok/s | 55031 ms | 4K |
| Reasoning | F | Too heavy | 5.9 tok/s | 38525 ms | 4K |
| RAG | F | Too heavy | 5.1 tok/s | 68789 ms | 4K |
How Nemotron 70B (70B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | F0 |
Q3_K_S | 3 | 34.3 GB | Low | F0 |
NVFP4 | 4 | 39.2 GB | Medium | F0 |
Q4_K_M | 4 | 42.7 GB | Medium | F0 |
Q5_K_M | 5 | 50.4 GB | High | F0 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Copy-paste commands to run Nemotron 70B on your machine.
Run
ollama run nemotron升级选项
RTX A6000 48GB最佳性价比Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 51%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA 升级Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 202%.
~$4,999 MSRP
NVIDIA A16 64GB经济之选Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$6,500 MSRP
NVIDIA H100 80GB最大飞跃Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
Yes, NVIDIA V100 32GB can run Nemotron 70B at Q2_K quantization (Very compromised (needs ~3.2 GB host RAM)). The recommended Q4_K_M requires 51.7 GB which exceeds available memory, but at Q2_K it needs only 36.3 GB. Expected decode speed: 14.2 tok/s.
Nemotron 70B (70B parameters) requires approximately 51.7 GB at Q4_K_M quantization. On NVIDIA V100 32GB, it fits at Q2_K using 36.3 GB.
The recommended quantization is Q4_K_M, but on NVIDIA V100 32GB the best fitting quantization is Q2_K, which uses 36.3 GB.
On NVIDIA V100 32GB, Nemotron 70B achieves approximately 14.2 tokens per second decode speed with a time-to-first-token of 13677ms using Q2_K quantization.
For coding workloads, Nemotron 70B on NVIDIA V100 32GB receives a F grade with 5.5 tok/s and 4K context.
On NVIDIA V100 32GB, Nemotron 70B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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<iframe src="https://willitrunai.com/embed/nemotron-70b-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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