Intern-S2-397B GGUF Quantized Models Released by bartowski

September 15, 2026September 18, 2026

At a Glance

Values determined by this site’s code at collection time. Dates are JST.

Overview

bartowski has released “bartowski/Intern-S2-397B-GGUF", a GGUF quantized version repository of the multimodal foundation model “Intern-S2-397B" developed by Shanghai AI Laboratory.

This repository is quantized using llama.cpp (release b10896) and includes a Multi-Token Prediction (MTP) layer to speed up inference along with projector files (mmproj) for image input.

Specifications

• Parameters: 403B (original model architecture is Qwen3_5MoeForConditionalGeneration)
• Context Length: Up to 256K tokens (during text inference evaluation of the original model), up to 64K tokens (during multimodal inference evaluation of the original model)
• License: apache-2.0

Strengths and Use Cases

The original model, “Intern-S2-397B", is a multimodal foundation model designed for scientific intelligence and long-horizon agents.

According to the model card, it adopts a visual pre-training approach that learns directly from scientific literature pages, modeling symbolic semantics and visual relationships in a shared representation space. Trained through scientific reinforcement learning tasks across over 20 domains, it excels at specialized scientific tasks such as biomolecular interaction design and material structure generation, as well as general and scientific autonomous agent tasks leveraging sandbox environments. It also supports switching between thinking modes, tool calling, and time-series data inference (such as seismic event detection and power load forecasting).

Note that this release is a quantized version converting the original model into GGUF format, and minor accuracy changes may occur due to quantization.

Hardware Requirements

Estimated requirements (calculated by Local Model Watch) — 403.4B parameters (taken from the base model internlm/Intern-S2-397B)

Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.

Recent Models in the Same Size Class

Models with over 40B parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.

How to Get It

Distributed in GGUF format, it can be obtained via the Hugging Face CLI and other tools. It is compatible with inference engines such as llama.cpp, LM Studio, koboldcpp, ramalama, Jan AI, Text Generation Web UI, LoLLMs, and Atomic Chat.

To obtain the recommended Q4_K_M quantization file and start the llama.cpp server, you can use the following commands:

pip install -U "huggingface_hub[cli]"
hf download bartowski/Intern-S2-397B-GGUF --include "Intern-S2-397B-Q4_K_M/*" --local-dir./

One-liner execution and server startup in llama.cpp:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/Intern-S2-397B-GGUF:Q4_K_M

Note that when using speculative decoding via MTP, add the --spec-type draft-mtp startup option. When loading image inputs manually, specify the included mmproj-Intern-S2-397B-f16.gguf or similar with --mmproj.

Related Articles

• Orion-26B-A4B-v1.1 GGUF Quants Released by bartowski
• bartowski/nex-agi_Nex-N2.5-mini-GGUF: Specs and Hardware
• Salience-27B-R6 GGUF Released by bartowski
• Pantheon-Reasoning-26B-A4B-1.1-V2 GGUF Quantizations

Sources

• https://huggingface.co/bartowski/Intern-S2-397B-GGUF
• https://huggingface.co/internlm/Intern-S2-397B