{
  "video_id": "reddit_1uotkm7",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "New model: GigaChat3.5-432B-A28B (with day-0 GGUF support!)",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1uotkm7/new_model_gigachat35432ba28b_with_day0_gguf/",
  "external_url": null,
  "upload_date": "20260706",
  "published_at": "2026-07-06T10:34:31+00:00",
  "transcript": "New model from Sberbank: \n\n[https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B](https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B)\n\nBase version also available: [https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B-base](https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B-base)\n\nMost important is the're also made a GGUF version: [https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B-GGUF](https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B-GGUF) \n\nFor now it's not in master branch yet but one can build from this PR: [https://github.com/ggml-org/llama.cpp/pull/25342](https://github.com/ggml-org/llama.cpp/pull/25342)\n\n\n\n--- Top Comments ---\n\n\n[35 upvotes] DeepSeek 3.2 as a reference point of choice in benchmarks? Seems like a ~year behind the frontier models. \n\n[24 upvotes] It's a non reasoning model, that's quite rare those days. You need to take that into account when looking at benchmarks.\n\nI'm happy they open weighted intermediate checkpoints as well as base model, that's pretty rare, especially for models this big. That's like top 10% of openness of models on HF, the only thing missing is the exact dataset.\n\n[22 upvotes] >Compared to the previous flagship `GigaChat 3.1 Ultra` (700B), version 3.5 is \\~40% more compact yet stronger in code, mathematics, and agentic scenarios. It also uses roughly 4× less KV-cache per token, fits more than 2× more context into the same memory, and improves generation throughput by \\~20%.   \n  \n**Model architecture**   \n  \nGigaChat 3.5 Ultra uses a custom MoE architecture. The core change relative to 3.1 is a self-designed **hybrid architecture** and a matching training recipe: every acceleration feature (linear attention, MTP) was paired with a stabilizing mechanism so the model could be trained to full scale without loss of stability.   \n  \n**Hybrid attention: MLA + GatedDeltaNet**   \n  \nStandard attention grows more expensive with context length: the longer the request, the larger the KV-cache, and the more generation is bottlenecked on memory. GigaChat 3.5 introduces a hybrid design in which some layers remain regular MLA and the rest are linear-attention layers based on GatedDeltaNet. This preserves the strengths of full attention while lowering the cost of long context.   \n  \n**Multi-Token Prediction (MTP)**   \n  \nGigaChat Ultra 3.0 had a single MTP head; in Giga",
  "transcript_chars": 2348,
  "ingested_at": "2026-07-06T13:30:17.482976+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 90,
    "upvote_ratio": 0.81,
    "num_comments": 59,
    "author": "unbannedfornothing",
    "is_self": true
  }
}