Llama 3.1 Nemotron 70B
By NVIDIA · United States
Updated 2026-07-13
Overview
NVIDIA's RLHF tune of Llama 3.1 70B that topped Arena Hard at 85.0 at release. Strong alignment and instruction-following on familiar Llama foundations.
When to pick this model
- Instruction-heavy chat assistants needing strong alignment
- Deployments already standardized on the Llama 3.1 family
- Workloads where human-preference alignment beats raw benchmarks
- NVIDIA-stack deployments leveraging NIM and TensorRT-LLM
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 40 GB |
| Q5_K_M | 48 GB |
| Q8_0 | 75 GB |
| FP16 (no quantization) | 140 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Llama 3.1 Nemotron 70B spills past single consumer GPUs even at Q4_K_M (40 GB) — think dual-GPU or workstation cards. Stepping up to Q8_0 nearly doubles the footprint to 75 GB, and unquantized FP16 weights take 140 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Llama 3.1 Nemotron 70B needs roughly 64 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 6 tokens/sec on a mid-range card, and up to 20 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Llama 3.1 Nemotron 70B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Llama 3.1 Nemotron 70B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 40 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 40 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 40 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 40 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 40 GB at Q4_K_M |
Which GPU should you buy to run Llama 3.1 Nemotron 70B?
To run Llama 3.1 Nemotron 70B locally at Q4, you need ~40 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Published benchmark scores
| Benchmark | Score |
|---|---|
| Arena Hard | 85 |
| AlpacaEval 2 LC | 57.6 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Arena Hard 85.0 — topped the leaderboard at release
- AlpacaEval 2 LC 57.6
- MT-Bench 8.98
- Strong RLHF on real human preference data
Limitations
- Llama 3.1 Community License with MAU clause
- Hugging Face gated access
- Now overtaken on reasoning by Qwen 2.5 72B and R1 distills
- ~42GB at Q4 — needs dual 24GB GPUs
Typical workloads
In our catalog grid, Llama 3.1 Nemotron 70B is filed under Post-RLHF Chat, Agents, Assistance — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Llama 3.1 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense Llama 3.1 70B · intensive NVIDIA RLHF
Training: RLHF on human preferences.
An excellent RLHF tune of Llama 3.1 70B — still strong for alignment-heavy chat, though reasoning specialists have since pulled ahead.
Quick start
ollama run nemotron:70bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does Llama 3.1 Nemotron 70B need?
At the recommended Q4_K_M quantization, Llama 3.1 Nemotron 70B needs about 40 GB of VRAM. Q8_0 takes 75 GB, and unquantized FP16 weights take 140 GB.
Can Llama 3.1 Nemotron 70B run without a GPU?
Yes — with roughly 64 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Llama 3.1 Nemotron 70B support?
Llama 3.1 Nemotron 70B supports a 125k-token context window (128,000 tokens).
Can I use Llama 3.1 Nemotron 70B commercially?
Llama 3.1 Nemotron 70B ships under the Llama 3.1 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Llama 3.1 Nemotron 70B on consumer hardware?
Our compatibility engine estimates on the order of 6 tokens/sec on a mid-range GPU and up to 20 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Llama 3.1 Nemotron 70B should I download first?
Start with Q4_K_M (40 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.