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// rednote dots · Text LLM

dots.llm1.base: VRAM & local deployment

142B total · 14B active (MoE) parameters. About 104 GB at 4-bit and 321 GB at BF16 with one 8K-token request.

released 2026-10-06licence: Commercial OKmemory figures are estimates

01Specs

Publisherrednote hi lab · rednote-hilab
Family / seriesrednote dots · dots.llm1.base
TypeText LLM (text-generation)
Parameters142B total · 14B active (MoE) Source: official model card“activates 14B parameters out of a total of 142B parameters”
Context length32K tokens (32,768)max_position_embeddings in config.json
Architecture62 layers · GQA · MoE
Released weightsFP32 · 6.2 GBdownload
LicenceMIT · Commercial OKPermissive licence (Apache-2.0 / MIT / CC-BY / OpenMDW): commercial use allowed.
Released2026-10-06 (Hugging Face repo created)

02How much VRAM it needs (estimate)

Total = weights + 10% + 0.5GB overhead + KV cache for one request (FP16 KV). More concurrent users multiply the KV column.

PrecisionWeightsTotal @ 8KTotal @ 32KSmallest GPUMac
BF16 / FP16 (original)284 GB 321 GB346 GB 4 × RTX PRO 6000 (384GB)Mac 512GB
FP8 / INT8 / Q8_0151 GB 175 GB200 GB 4 × RTX PRO 6000 (384GB)Mac 512GB
4-bit (Q4_K_M / AWQ / GPTQ-Int4)86.1 GB 104 GB128 GB H200 141GBMac 256GB

KV cache per request: 8K 8.3 GB · 32K 33.3 GB

Bits per weight: BF16 16, FP8 8.5, 4-bit 4.85. “Fits” = total ≤ 90% of memory; Mac GPU can use ~2/3 of unified memory (≤36GB) or ~3/4 (larger). For MoE, all experts must be in memory, so size by total parameters. Method →

03How to run it

Only runtimes the publisher or the runtime’s official library names for this model.

Which inference engine to choose →

04Official repositories & quantised builds

1 official repo(s) from rednote-hilab.

06Sources

Data checked 2026-10-07. Parameters, context and licence are copied from the official repository; memory figures are our estimates.

Data updated: Self-host open models(UTC+8)