ai21labs/AI21-Jamba2-Mini-FP8
published by AI21 Labshuggingface.co/ai21labs/AI21-Jamba2-Mini-FP8
This is a quantization of AI21-Jamba2-Mini, not an independent model. Parameters, benchmarks, prices and lineage are recorded on the canonical model. Open AI21-Jamba2-Mini →
Updated 2 h ago · first seen 11 Sept 2026
model_01M294WVM1R5K51PD7A4GVSW9D
- File size
- Format
- Downloads
- Published
Artifact facts
- Weights dtype
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- File size
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Downloads
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 6 h agomedium
- Likes
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 6 h agomedium
- Hugging Face repo
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Pipeline tag
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Library name
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Gated
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- License
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Release date
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Last modified
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Model card
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Tags
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
Hardware fit (this packaging)
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.
Provenance
Attributed facts
23
Source tiers
T223
Freshest observation
2 h ago
Conflicts
None
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.