Nemotron-4 340B Instruct
NVIDIA's reward-modelling research vehicle. Trained primarily to be a synthetic-data-generation specialist rather than a chat-first model. Useful for teams building instruction-tuning datasets at scale.
- Parameters
- 340B
- Context length
- 4K
- Modality
- text
- Released
- 2024-06-14
Memory & hardware
- VRAM (fp16)
- 680 GB
- VRAM (Q4)
- 204 GB
- Recommended
- 8× H100 80GB
- Quantizations
- fp16, fp8
License: Llama 3 Community License
- SPDX
- —
- Commercial use
- Yes
- Modification
- Yes
- Redistribution
- Yes
Benchmarks
Hosted inference pricing
No hosted pricing listed — this model is currently self-host-only on this site.
Run it yourself
Drop-in commands for the three most common open-source inference paths. The Ollama tag is a best-effort match against the registry; verify the size variant before pulling.
vllm serve nvidia/Nemotron-4-340B-Instruct
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-4-340B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"nvidia/Nemotron-4-340B-Instruct", device_map="auto", torch_dtype="auto"
)nvidia/Nemotron-4-340B-Instruct Related models
Same family or similar size — useful when shopping around.
NVIDIA's RLHF-tuned Llama 3.1 70B. Tops several Arena-style human-preference leaderboards and shipped with NVIDIA's reward-model research. Inherits the Llama 3 community licence.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
xAI's first open-weights release: a 314B-parameter mixture-of-experts model. Apache 2.0 licensed. Largely a research artefact at this size — most users will run smaller models for production — but useful as a permissively-licensed reference for MoE research.
- Context
- 8K
- License
- apache-2-0
- VRAM Q4
- 188.4 GB
Hybrid Mamba-Transformer-MoE model with native 256K context (effective beyond 140K). 94B active parameters out of 398B total. The state-space-model layers give it linear-time scaling with sequence length, making it interesting for very long contexts. Licensed under AI21's open model licence, which permits most commercial use.
- Context
- 256K
- License
- jamba-open
- VRAM Q4
- 238.8 GB
Coding-focused MoE model with 21B active parameters out of 236B total. Supports 338 programming languages with strong performance across mainstream stacks (Python, TypeScript, Go, Rust, Java, C++) and competent results on niche languages where most open models falter. The DeepSeek licence applies — commercial use permitted with some application restrictions.
- Context
- 128K
- License
- deepseek
- VRAM Q4
- 141.6 GB
Scaled-up Mixtral with 22B-parameter experts. ~39B active parameters out of 141B total. Strong long-context performance and competitive coding scores. Apache 2.0 makes it attractive for self-hosting where the licence terms of Llama 3 are a non-starter.
- Context
- 66K
- License
- apache-2-0
- VRAM Q4
- 84.6 GB
Cohere's flagship 104B model. RAG-focused with native multilingual support across ~10 high-resource languages. CC-BY-NC weights; commercial use via Cohere's hosted API.
- Context
- 128K
- License
- mrl
- VRAM Q4
- 62.4 GB