Local LMs

montez

Amazon ALoDLM

Hugging Face: https://huggingface.co/amazon/ALoDLM-8B, https://huggingface.co/amazon/ALoDLM-1.7B
GitHub: https://github.com/amazon-science/ALoDLM
Website: https://alo-dlm.github.io/
Technical report: https://arxiv.org/abs/2610.04198

Developer: Amazon
Released: October 2026
Variants: 8B / 1.7B
Parameters: 8B / 1.7B
Architecture: adaptively looped masked diffusion language model initialized from Qwen3-8B and Qwen3-1.7B; shared transformer layers applied recurrently with a learned halting policy; 8B: 36 layers / 1.7B: 28 layers
License: CC-BY-NC-4.0
Modalities: Text
Runs on: 8B: Desktop GPU, 24GB+ VRAM / 1.7B: Laptop, 8GB+ VRAM; NVIDIA only
Formats: safetensors BF16
On disk: 8B: 16.4GB BF16 / 1.7B: 4.08GB BF16

montez

Ai2 AstaBrief 8B

Hugging Face: https://huggingface.co/allenai/AstaBrief_8B, https://huggingface.co/allenai/AstaBrief_8B_SFT
Website: https://allenai.org/blog/astabrief

Developer: Ai2
Released: October 2026
Variants: AstaBrief-8B (DPO) / AstaBrief-8B-SFT
Parameters: 8B
Context: 40,960 tokens
Architecture: Qwen3-8B base, 36 layers; turns a research question and retrieved literature excerpts into a cited scientific report
License: Apache 2.0
Modalities: Text
Runs on: Desktop GPU
Formats: safetensors BF16
On disk: 16.38GB BF16

montez

IFM Jais 3

Hugging Face: https://huggingface.co/inception42/Jais-3-0.9B, https://huggingface.co/inception42/Jais-3-3.7B, https://huggingface.co/inception42/Jais-3-7B, https://huggingface.co/inception42/Jais-3-32B, https://huggingface.co/inception42/Jais-3-MoVA-36B-A4B
Website: https://ifm.ai/blog/jais3/

Developer: Institute of Foundation Models (IFM), MBZUAI, and Inception42
Released: October 2026
Variants: 0.9B / 3.7B / 7B / 32B / MoVA-36B-A4B
Parameters: 0.9B / 3.7B / 7B / 32B / 36B total, 4B active
Context: 0.9B: 131,072 tokens / 3.7B, 7B, 32B and MoVA-36B-A4B: 524,288 tokens
Architecture: K2 Horizon base adapted to Arabic; 0.9B, 3.7B, 7B, 32B: dense decoder-only, GQA, 28 / 36 / 36 / 64 layers; MoVA-36B-A4B: MoE with Mixture-of-Values attention, 48 layers, 100 FFN experts with 8 per token plus 1 shared
License: Apache 2.0
Modalities: Text
Runs on: 0.9B: Smartphone / 3.7B and 7B: Smartphone, Laptop / 32B and MoVA-36B-A4B: Laptop, 32GB+ unified memory
Formats: safetensors BF16, safetensors FP8, GGUF (Q4_K_M, Q5_0, Q5_K_M, Q6_K, Q8_0, BF16), MLX 4-bit, 8-bit, bf16
On disk: 0.9B: 0.67GB Q4_K_M / 3.7B: 3.16GB Q4_K_M / 7B: 5.59GB Q4_K_M / 32B: 21.08GB Q4_K_M / MoVA-36B-A4B: 22.37GB Q4_K_M

montez

inclusionAI Ming-3.0-audio-tiny

Hugging Face: https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny
GitHub: https://github.com/inclusionAI/Ming-3.0-audio-flash

Developer: inclusionAI, Ant Group
Released: October 2026
Parameters: 9.3B total, 2.7B active
Context: 131,072 tokens
Architecture: native audio-language MoE; BailingMoE v3 language backbone with 128 experts, 32-layer audio encoder with projector, optional Talker for synthesized speech output
License: MIT
Modalities: Text + Audio
Runs on: Desktop GPU; 1 GPU for text output, 2 GPUs for synthesized speech
Formats: BF16 safetensors
On disk: 17.74GB BF16

montez

inclusionAI Realtime-Venus

Hugging Face: https://huggingface.co/inclusionAI/Realtime-Venus
GitHub: https://github.com/inclusionAI/Realtime-Venus
Website: https://realtime-venus.github.io/
Technical report: https://arxiv.org/abs/2609.13814

Developer: Venus Team, Ant Group, and Tsinghua University
Released: September 2026
Variants: Realtime-Venus-Omni / Realtime-Venus-Audio
Parameters: 9B each
Context: 40,960 tokens
Architecture: MiniCPM-o 4.5 base; Qwen3-8B language backbone, Whisper-Medium audio encoder, SigLIP2 visual encoder (Omni only), streaming flow-matching speech decoder; full-duplex streaming
License: Apache 2.0
Modalities: Omni: Text + Image + Video + Audio / Audio: Text + Audio
Runs on: Desktop GPU; NVIDIA only
Formats: BF16 safetensors
On disk: Omni: 20.08GB BF16 safetensors / Audio: 19.99GB BF16 safetensors

montez

Zhongguancun ZGCM-1-7B

Hugging Face: https://huggingface.co/zgcagi/ZGCM-1-7B
GitHub: https://github.com/zgcagi/ZGCM-1
Technical report: https://arxiv.org/abs/2609.13356

Developer: Zhongguancun Academy and Zhongguancun Institute of Artificial Intelligence
Released: September 2026
Parameters: 7.39B
Context: 262,144 tokens
Architecture: trained from scratch; decoder-only dense, 32 layers, 27 gated sliding-window layers plus 5 global-attention layers, GQA
License: MIT
Modalities: Text
Runs on: Desktop GPU
Formats: BF16 safetensors
On disk: 14.79GB BF16

montez

RWKV RWKV7-G1k

Hugging Face: https://huggingface.co/RWKV/RWKV7-G1k-1.5B-20260930, https://huggingface.co/RWKV/RWKV7-G1k-2.9B-20260930, https://huggingface.co/RWKV/RWKV7-G1k-7.2B-20260930, https://huggingface.co/RWKV/RWKV7-G1k-13.3B-20260930
GitHub: https://github.com/BlinkDL/RWKV-LM
Technical report: https://arxiv.org/abs/2503.14456

Developer: RWKV (Bo Peng)
Released: September 2026
Variants: 1.5B / 2.9B / 7.2B / 13.3B
Parameters: 1.5B / 2.9B / 7.2B / 13.3B
Architecture: RWKV-7 Goose, attention-free recurrent model with a constant-size recurrent state; 24 / 32 / 32 / 61 layers
License: Apache 2.0
Modalities: Text
Runs on: 1.5B and 2.9B: Smartphone, Laptop / 7.2B: Desktop GPU / 13.3B: Workstation GPU
Formats: BF16 safetensors, PyTorch .pth
On disk: 1.5B: 3.06GB / 2.9B: 5.90GB / 7.2B: 14.40GB / 13.3B: 26.54GB BF16 safetensors

montez

DeepSeek-V4.1-Flash-1017 (third-party builds)

Hugging Face: https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF, https://huggingface.co/GreenBitAI/DeepSeek-V4.1-Flash-1017-4bit-paged
GitHub: https://github.com/GreenBitAI/gbx-lm, https://github.com/ggml-org/llama.cpp/pull/30817
X: https://x.com/UnslothAI/status/2112212605930778830
Docs: https://unsloth.ai/docs/models/deepseek-v4.1-flash

Developer: DeepSeek (model), Unsloth and GreenBitAI (builds)
Released: October 2026
Variants: Unsloth GGUF UD-IQ1_S to UD-Q8_K_XL / GreenBitAI 4-bit expert-paged
Parameters: 552B total, 8B active for input, 16B active for output
Context: 1,048,576 tokens
Architecture: causal encoder-decoder MoE, 40 layers, 384 routed experts with 6 per token plus 1 shared; sliding-window plus compressed sparse attention, engram n-gram lookups, Hyper-Connections; 32-layer vision encoder
License: MIT
Modalities: Text + Image
Runs on: Laptop or desktop, 128GB+ RAM + VRAM or unified memory with Engram table on SSD (Unsloth 1-bit), 256GB+ (Unsloth 3-bit) / Mac Studio 512GB, or smaller Macs and DGX Spark with experts paged from SSD (GreenBitAI, Apple Silicon or DGX Spark only)
Formats: GGUF 1-bit to 8-bit (Unsloth) / safetensors mixed 4/8-bit, expert-paged for gbx_lm (GreenBitAI)
On disk: 233GB UD-IQ1_S, 250.4GB UD-IQ1_M, 394.7GB UD-IQ3_XXS, 508.4GB UD-Q8_K_XL / 436GB 4-bit paged