Nemotron 70B needs ~50.9 GB but NVIDIA A30 24GB only has 24.0 GB. Try a smaller quantization or lighter model.
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
26.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.9 tok/s
TTFT
67741 ms
Safe context
4K
Memory
50.9 GB / 24.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 50.9 GB, but this setup only exposes 24.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.2 tok/s | 33317 ms | 4K |
| Coding | F | Too heavy | 2.9 tok/s | 67741 ms | 4K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 101289 ms | 4K |
| Reasoning | F | Too heavy | 2.9 tok/s | 80058 ms | 4K |
| RAG | F | Too heavy | 2.8 tok/s | 126612 ms | 4K |
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 A30 24GB (24.0 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 |
Upgrade options
RTX A6000 48GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 207%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 514%.
~$4,999 MSRP
NVIDIA A16 64GBBudget pickMakes 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 80GBBiggest leapMakes 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
No, Nemotron 70B requires more memory than NVIDIA A30 24GB provides.
Nemotron 70B (70B parameters) requires approximately 50.9 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 A30 24GB, Nemotron 70B achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 67741ms using Q4_K_M quantization.
For coding workloads, Nemotron 70B on NVIDIA A30 24GB receives a F grade with 2.9 tok/s and 4K context.
On NVIDIA A30 24GB, Nemotron 70B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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