Model library / InternScience
MLExpert is a 35 billion-parameter mixture-of-experts model for autonomous machine-learning engineering tasks.
Mixture of experts, count not published
Context length in tokens, as published
Weights at the released precision
Licence declared on the repository
Overview
MLExpert is a 35 billion-parameter mixture-of-experts model released by InternScience for autonomous machine-learning engineering. It is designed to plan and run experiments, generate and revise code, and iteratively improve solutions through multi-turn interaction with execution environments. The model was trained with hierarchical agentic SFT and a windowed interaction reinforcement learning stage.
Deploying MLExpert requires handling a 34.7 billion-parameter checkpoint with a 262 k token context window. The model is released under the Apache-2.0 licence, but the weight format is not published, and the expert count is unspecified. Its size and long context make it suitable for server-grade hardware supporting large memory, while the open licence permits commercial use.
Specification
| Repository | InternScience/MLExpert |
| Publisher | InternScience |
| Published | 1 October 2026 |
| Architecture | Qwen3_5MoeForConditionalGeneration |
| Model type | qwen3_5_moe |
| Parameters | 34,660,610,688 |
| Layers | 40 |
| Hidden size | 2,048 |
| Attention heads | 16, 2 key/value heads |
| Context length | 262,144 tokens |
| Vocabulary | 248,320 tokens |
| Licence | apache-2.0 |
Memory
Parameter count multiplied by the bytes each format uses. The key-value cache comes on top.
| Precision | Weights | Notes |
|---|---|---|
| bf16 | 64.6 GiB | Full precision as released |
| fp8 | 32.3 GiB | 8-bit, near-lossless on most models |
| int4 | 16.1 GiB | 4-bit, smallest footprint |
The cache costs 80 KiB per token at 16-bit, so the full 262,144-token context of one request needs about 20.0 GiB. Concurrency multiplies that number, not the weights.
Hardware
At the released precision, with 90% of the card given to the server and 2.5 GiB kept for the runtime. The context column is what is left for the cache on that setup.
| GPU | Cards needed | Free for cache | Context it holds |
|---|---|---|---|
| RTX 4090 24GB | 4 | 11.8 GiB | about 155,183 tokens |
| RTX 5090 32GB | 4 | 40.6 GiB | the full 262,144 tokens |
| L40S 48GB | 2 | 16.8 GiB | about 220,719 tokens |
| A100 80GB | 1 | 4.9 GiB | about 64,744 tokens |
| H100 80GB | 1 | 4.9 GiB | about 64,744 tokens |
| RTX PRO 6000 96GB | 1 | 19.3 GiB | about 253,487 tokens |
| H200 141GB | 1 | 59.8 GiB | the full 262,144 tokens |
Serving
A vLLM launch line for the shape above. Check the model card for a runtime the publisher recommends.
vllm serve InternScience/MLExpert \ --max-model-len 151552 \ --gpu-memory-utilization 0.90
Questions
The weights take about 65 GiB at the released precision. That fits on one A100 80GB, which leaves roughly 5 GiB for the key-value cache.
The repository declares apache-2.0. Read the licence text before commercial use: the name alone does not tell you what is allowed.
The configuration allows 262,144 tokens. Whether the whole window is usable depends on the memory left for the cache, which the hardware table works out per card.
This page is a reference, and Aiqre does not serve this model today. We deploy open-weight models on dedicated EU hardware on request, so if you want an OpenAI-compatible endpoint for it, email hello@aiqre.com with the model name and roughly what volume you expect.
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Hosting
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