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ArtifactquantizationCOMPRESSED-TENSORSOpen weightsof K-EXAONE-236B-A23B

LGAI-EXAONE/K-EXAONE-236B-A23B-FP8

published by LG AI Researchhuggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B-FP8

This is a quantization of K-EXAONE-236B-A23B, not an independent model. Parameters, benchmarks, prices and lineage are recorded on the canonical model. Open K-EXAONE-236B-A23B →

data quality39

Updated 4 h ago · first seen 12 Sept 2026

artifact_01M2AS5J6M44XZZ53F6WGWZ5J8

File size
Format
Downloads
Published

Artifact facts

Quantization format

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Weights dtype

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

File size

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Downloads

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Likes

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Hugging Face repo

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 6 h agomedium

Base model

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Pipeline tag

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 6 h agomedium

Library name

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Access

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 6 h agomedium

License

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Release date

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Last modified

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 6 h agomedium

Tags

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 5 h agomedium

Hardware fit (this packaging)

Estimated

9 of 37 device × quantization combinations fit.

Run locally: your machine →
Estimated hardware fit
HardwareQuantizationDevice memoryEst. memoryFits
Apple M3 Ultra4bit—136.9 GB est.Yes
Mac Studio (Apple M5 Ultra)4bit—136.9 GB est.Yes
Apple M2 Ultra4bit—136.9 GB est.Yes
NVIDIA H2004bit141 GB136.9 GB est.Yes
NVIDIA H200 NVL4bit141 GB136.9 GB est.Yes
NVIDIA B2004bit180 GB136.9 GB est.Yes
AMD Instinct MI300X4bit192 GB136.9 GB est.Yes
AMD Instinct MI325X4bit256 GB136.9 GB est.Yes
NVIDIA DGX B2004bit1,440 GB136.9 GB est.Yes
Apple M1 Ultra4bit—136.9 GB est.No
Apple M3 Max4bit—136.9 GB est.No
Apple M4 Max4bit—136.9 GB est.No
Mac Studio (Apple M5 Max)4bit—136.9 GB est.No
MacBook Pro (Apple M5 Max)4bit—136.9 GB est.No
Apple M2 Max4bit—136.9 GB est.No
Apple M1 Max4bit—136.9 GB est.No
Apple M4 Pro4bit—136.9 GB est.No
Mac mini (Apple M5 Pro)4bit—136.9 GB est.No
MacBook Pro (Apple M5 Pro)4bit—136.9 GB est.No
Apple M3 Pro4bit—136.9 GB est.No
Apple M1 Pro4bit—136.9 GB est.No
Apple M2 Pro4bit—136.9 GB est.No
Apple M44bit—136.9 GB est.No
iMac (Apple M4)4bit—136.9 GB est.No
Mac mini (Apple M6)4bit—136.9 GB est.No
MacBook Air (Apple M5)4bit—136.9 GB est.No
MacBook Pro (Apple M5)4bit—136.9 GB est.No
Apple M24bit—136.9 GB est.No
Apple M34bit—136.9 GB est.No
Apple M14bit—136.9 GB est.No
NVIDIA GeForce RTX 30904bit24 GB136.9 GB est.No
NVIDIA GeForce RTX 40904bit24 GB136.9 GB est.No
NVIDIA GeForce RTX 50904bit32 GB136.9 GB est.No
NVIDIA A100 80GB4bit80 GB136.9 GB est.No
NVIDIA H100 SXM4bit80 GB136.9 GB est.No
NVIDIA H100 NVL4bit94 GB136.9 GB est.No
NVIDIA DGX Spark4bit128 GB136.9 GB est.No
Assumptions (7)
  • Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead (or the observed artifact file size when one is recorded).
  • bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
  • KV cache: 2 × layers × kv_heads × head_dim × 2 bytes × context × batch when the architecture is known; otherwise 0.5 GB per 8 192 tokens (× batch), independent of architecture (GQA/MLA models need less).
  • A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
  • Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
  • Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
  • Multi-GPU: device memories are summed; interconnect bandwidth, tensor-parallel replication and pipeline bubbles are not modelled.

Timeline

Full timeline →

Provenance

Attributed facts

32

Source tiers

T232

Freshest observation

5 h ago

Conflicts

None

Source documents
SourceDocumentTypeTierLast observedSnapshots
Hugging Face Hub (public pages, model cards, papers)huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B-FP8/raw/main/README.md model_cardT2· Quality secondary5 h ago1
Hugging Face Hub (public pages, model cards, papers)huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B-FP8 model_pageT2· Quality secondary5 h ago1
Hugging Face Hub (public pages, model cards, papers)huggingface.co/models?author=LGAI-EXAONE&p=0&sort=downloads listingT2· Quality secondary6 h ago2

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.