Minimum VRAM
140GB
FP16 (full model) • Q4 option ≈ 35GB
Best Performance
AMD Instinct MI300X
~102 tok/s • FP16
Most Affordable
Apple M2 Ultra
FP16 • ~14 tok/s • From $5,999
Quick answer: NVIDIA Llama 3 1 Nemotron 70B Instruct HF needs roughly 35GB VRAM for Q4_K_M and 53GB for Q5_K_M. Use Q8 (70GB) or FP16 (140GB) for higher quality output.
Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.
Ready to buy?
See our tested GPU picks for running NVIDIA Llama 3 1 Nemotron 70B Instruct HF locally.
Best GPU for Running LLMs →Filter by quantization, price, and VRAM to compare performance estimates.
Showing FP16 compatibility. Switch tabs to explore other quantizations.
| GPU | Speed | VRAM Requirement | Typical price |
|---|---|---|---|
AMD Instinct MI300XEstimated AMD | ~102 tok/s FP16 | 140GB VRAM used192GB total on card | $15,000View GPU → |
NVIDIA H200 SXM 141GBTight VRAM NVIDIA | ~92 tok/s FP16 | 140GB VRAM used141GB total on card | $35,000View GPU → |
NVIDIA H100 SXM5 80GBEstimated NVIDIA | ~66 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used80GB total on card | $30,000View GPU → |
AMD Instinct MI250XEstimated AMD | ~64 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used128GB total on card | $11,000View GPU → |
NVIDIA H100 PCIe 80GBEstimated NVIDIA | ~42 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used80GB total on card | $25,000View GPU → |
RTX 5090Data coming soon NVIDIA | ~40 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used32GB total on card | $1,999View GPU → |
NVIDIA A100 80GB SXM4Estimated NVIDIA | ~39 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used80GB total on card | $11,000View GPU → |
AMD Instinct MI210Estimated AMD | ~32 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used64GB total on card | $6,000View GPU → |
NVIDIA A100 40GB PCIeEstimated NVIDIA | ~30 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used40GB total on card | $9,000View GPU → |
RTX 4090Data coming soon NVIDIA | ~24 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used24GB total on card | $1,599View GPU → |
NVIDIA RTX 6000 AdaEstimated NVIDIA | ~24 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used48GB total on card | $6,999View GPU → |
NVIDIA L40Estimated NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used48GB total on card | $7,999View GPU → |
NVIDIA L40SEstimated NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used48GB total on card | $10,000View GPU → |
RTX 5080Data coming soon NVIDIA | ~21 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $1,199View GPU → |
RTX 3090Data coming soon NVIDIA | ~21 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used24GB total on card | $1,499View GPU → |
AMD Radeon Pro W7900Estimated AMD | ~19 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used48GB total on card | $3,999View GPU → |
RX 7900 XTXData coming soon AMD | ~19 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used24GB total on card | $999View GPU → |
RTX 5070 TiData coming soon NVIDIA | ~19 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $799View GPU → |
NVIDIA A6000Estimated NVIDIA | ~18 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used48GB total on card | $4,699View GPU → |
RTX 4080 SuperData coming soon NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $999View GPU → |
RTX 3080Data coming soon NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used10GB total on card | $699View GPU → |
NVIDIA A5000Data coming soon NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used24GB total on card | $2,399View GPU → |
RTX 4080Data coming soon NVIDIA | ~16 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $1,199View GPU → |
RX 7900 XTData coming soon AMD | ~16 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used20GB total on card | $899View GPU → |
RTX 4070 Ti SuperData coming soon NVIDIA | ~15 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $799View GPU → |
RTX 5070Data coming soon NVIDIA | ~14 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $599View GPU → |
Apple M2 UltraEstimated Apple | ~14 tok/s FP16 | 140GB VRAM used192GB total on card | $5,999View GPU → |
RX 9070 XTData coming soon AMD | ~13 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $599View GPU → |
RX 7800 XTData coming soon AMD | ~13 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $499View GPU → |
RX 7900 GREData coming soon AMD | ~12 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $649View GPU → |
AMD Radeon Pro W7800Data coming soon AMD | ~12 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used32GB total on card | $2,499View GPU → |
RTX 4070 TiData coming soon NVIDIA | ~12 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $799View GPU → |
RTX 4070 SuperData coming soon NVIDIA | ~12 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $599View GPU → |
RX 9070Data coming soon AMD | ~12 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $499View GPU → |
Intel Arc A770 16GBData coming soon Intel | ~11 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $349View GPU → |
RTX 4070Data coming soon NVIDIA | ~11 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $599View GPU → |
RX 6900 XTData coming soon AMD | ~11 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $999View GPU → |
RX 6800 XTData coming soon AMD | ~11 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $649View GPU → |
Intel Arc A750Data coming soon Intel | ~10 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used8GB total on card | $289View GPU → |
NVIDIA A4000Data coming soon NVIDIA | ~10 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $999View GPU → |
RTX 3070Data coming soon NVIDIA | ~10 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used8GB total on card | $499View GPU → |
Intel Arc B580Data coming soon Intel | ~10 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $249View GPU → |
Apple M4 MaxEstimated Apple | ~10 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used128GB total on card | $3,999View GPU → |
RX 7700 XTData coming soon AMD | ~9 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $449View GPU → |
Intel Arc B570Data coming soon Intel | ~8 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used10GB total on card | $219View GPU → |
Intel Arc Pro A60Data coming soon Intel | ~8 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $599View GPU → |
NVIDIA L4Data coming soon NVIDIA | ~8 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used24GB total on card | $5,000View GPU → |
RTX 3060 12GBData coming soon NVIDIA | ~8 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used12GB total on card | $329View GPU → |
Apple M3 MaxEstimated Apple | ~7 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used128GB total on card | $3,999View GPU → |
Apple M2 MaxEstimated Apple | ~7 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used96GB total on card | $3,199View GPU → |
RTX 4060 Ti 16GBData coming soon NVIDIA | ~7 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $499View GPU → |
RTX 4060 Ti 8GBData coming soon NVIDIA | ~7 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used8GB total on card | $399View GPU → |
RTX 4060Data coming soon NVIDIA | ~6 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used8GB total on card | $299View GPU → |
RX 7600 XTData coming soon AMD | ~6 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used16GB total on card | $329View GPU → |
RX 7600Data coming soon AMD | ~6 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used8GB total on card | $269View GPU → |
Intel Arc Pro A40Data coming soon Intel | ~6 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used6GB total on card | $399View GPU → |
Apple M4 ProEstimated Apple | ~5 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used64GB total on card | $1,999View GPU → |
AMD Ryzen AI Max+ 395Estimated AMD | ~5 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max 385Estimated AMD | ~5 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max Pro 385Estimated AMD | ~5 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used128GB total on card | EnterpriseView GPU → |
Apple M2 ProData coming soon Apple | ~4 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used32GB total on card | $1,999View GPU → |
Apple M3 ProTight VRAM Apple | ~3 tok/s FP16⚠ Insufficient VRAM | 140GB VRAM used36GB total on card | $1,999View GPU → |
NVIDIA Llama 3 1 Nemotron 70B Instruct HF 70B parametre içerir ve 35GB VRAM gerektirir - choose the best GPU for your needs
For Better Performance
Run NVIDIA Llama 3 1 Nemotron 70B Instruct HF faster with AMD Instinct MI300X. For just $130 more, significantly boost your tokens/sec performance.
Hardware requirements and model sizes at a glance.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 35GB (Q4) | 70GB (Q8) | 140GB (FP16) |
| RAM | 53GB | 105GB | 175GB |
| Disk | 28GB | 56GB | - |
| Model size | 35GB (Q4) | 70GB (Q8) | 140GB (FP16) |
| CPU | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Common questions about running NVIDIA Llama 3 1 Nemotron 70B Instruct HF locally
Llama 3 70B balances top-tier reasoning quality with manageable on-premise requirements. This guide explains the hardware you need to run the model smoothly and how to optimize for your desired quantization tier.
Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).
Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.
Q4_K_M and Q5_K_M are GGUF quantization formats that balance quality and VRAM usage. Q4_K_M uses about 35GB VRAM. Q5_K_M uses about 53GB VRAM and keeps more accuracy. Q8 (~70GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for NVIDIA Llama 3 1 Nemotron 70B Instruct HF.
Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.
See how NVIDIA Llama 3 1 Nemotron 70B Instruct HF compares to other popular models.