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Llama 3 1 405b Instruct Fp8

llama-3-1-405b-instruct-fp8
  • Open weights
  • Llama 3.1 Community licence
  • 405.9B params
  • Released 2024-07-23(curated)
4/27axes covered
Overall · LMArena
1,334#224 of 396
Best percentile
43.7Overall
Cohort standing
46.0median 36.3

What the data says

Benchmarked on 4 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 1.2 percentile points between its best and worst axis — 43.7 pct on Overall versus 42.5 pct on Coding.

  • Overall43.7 pct1,334Verify →
  • Instruction following43.4 pct1,315Verify →
  • Hard prompts42.7 pct1,337Verify →
  • Coding42.5 pct1,369Verify →
  • 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
  • LiveBenchNot 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

Repositorymeta-llama/Llama-3.1-405B-Instruct-FP8
Parameters405.9B
ArchitectureLlamaForCausalLM
LicenceLlama 3.1 Community
AccessGated — you must accept the repo's terms before downloading

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "meta-llama/Llama-3.1-405B-Instruct-FP8"
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-3.1-405b-instruct-fp8 stands at 46 (raw mean 43.1) — measured on 4 of 27 axes.

    standingtemplateVerify →

  • llama-3.1-405b-instruct-fp8 stands at 46 versus a cohort median of 36.3 across 131 models released within 180 days — above 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 →