Nemotron 70B needs ~54.9 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~12 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
Tight fit
Decode
11.9 tok/s
TTFT
16243 ms
Safe context
46K
Memory
54.9 GB / 64.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 11.9 tok/s | 8860 ms | 46K |
| Coding | B | Tight fit | 11.9 tok/s | 16243 ms | 46K |
| Agentic Coding | B | Tight fit | 11.9 tok/s | 23626 ms | 46K |
| Reasoning | B | Tight fit | 11.9 tok/s | 19196 ms | 46K |
| RAG | B | Tight fit | 11.9 tok/s | 29532 ms | 46K |
Inference speed
Estimated decode speed (tokens/sec) for Nemotron 70B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~18 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 18.0 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 16.5 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 14.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 12.6 | Too big |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 11.8 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 11.2 | Fits |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 9.5 | Heavy offload |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 8.7 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 7.7 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 5.9 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 5.7 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.6 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 4.3 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.7 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Nemotron 70B (70B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | B68 |
Q3_K_S | 3 | 34.3 GB | Low | B69 |
NVFP4 | 4 | 39.2 GB | Medium | B69 |
Q4_K_M | 4 | 42.7 GB | Medium | B69 |
Q5_K_MBest for your GPU | 5 | 50.4 GB | High | B69 |
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 nemotronUpgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA upgradeRaises estimated decode speed by about 593%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Yes, NVIDIA A16 64GB can run Nemotron 70B with a B grade (Tight fit). Expected decode speed: 11.9 tok/s.
Nemotron 70B (70B parameters) requires approximately 54.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron 70B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A16 64GB, Nemotron 70B achieves approximately 11.9 tokens per second decode speed with a time-to-first-token of 16243ms using Q4_K_M quantization.
For coding workloads, Nemotron 70B on NVIDIA A16 64GB receives a B grade with 11.9 tok/s and 46K context.
On NVIDIA A16 64GB, Nemotron 70B can safely use up to 46K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-70b-on-a16-64gb" 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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