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// Baichuan · Text LLM

Baichuan-M3-235B: VRAM & local deployment

235.1B (MoE) parameters. About 159 GB at 4-bit and 519 GB at BF16 with one 8K-token request.

released 2026-01-13licence: Commercial OKmemory figures are estimates

01Specs

PublisherBaichuan · baichuan-inc
Family / seriesBaichuan · Baichuan-M3
TypeText LLM (text-generation)
Parameters235.1B (MoE) Source: counted from official safetensors weights
Context length40K tokens (40,960)max_position_embeddings in config.json
Architecture94 layers · GQA · MoE
Released weightsBF16 · 470 GBdownload
LicenceApache-2.0 · Commercial OKPermissive licence (Apache-2.0 / MIT / CC-BY / OpenMDW): commercial use allowed.
Released2026-01-13 (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)470 GB 519 GB524 GB 8 × H100 (640GB)—
FP8 / INT8 / Q8_0250 GB 277 GB282 GB 4 × RTX PRO 6000 (384GB)Mac 512GB
4-bit (Q4_K_M / AWQ / GPTQ-Int4)143 GB 159 GB164 GB 4 × RTX PRO 6000 (384GB)Mac 256GB

KV cache per request: 8K 1.6 GB · 32K 6.3 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

4 official repo(s) from baichuan-inc.

05Other Baichuan models

All 2 →
ModelParams4-bit totalReleased
Baichuan-M2-32B32.8B24.5 GB2025-08-10

06Sources

Data checked 2026-02-09. Parameters, context and licence are copied from the official repository; memory figures are our estimates.

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