Skip to content
aiqbench

Nvidia · model passport

Llama 3 1 Nemotron 70b Instruct

llama-3-1-nemotron-70b-instruct
  • Open weights
  • Llama 3.1 licence
  • Released 2024-10-15(curated)
5/27axes covered
Overall · LMArena
1,299#267 of 396
Best percentile
33.6Hard prompts
Cohort standing
38.8median 39.0

What the data says

Benchmarked on 5 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 2.9 percentile points between its best and worst axis — 33.6 pct on Hard prompts versus 30.7 pct on Coding.

  • Hard prompts33.6 pct1,310Verify →
  • Overall32.8 pct1,299Verify →
  • Instruction following32.3 pct1,280Verify →
  • LiveBench30.9 pct43.1%Verify →
  • Coding30.7 pct1,328Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorynvidia/Llama-3.1-Nemotron-70B-Instruct
LicenceLlama 3.1

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nvidia/Llama-3.1-Nemotron-70B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • llama-3.1-nemotron-70b-instruct stands at 38.8 (raw mean 32.1) — measured on 5 of 27 axes.

    standingtemplateVerify →

  • llama-3.1-nemotron-70b-instruct stands at 38.8 versus a cohort median of 39 across 132 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →