Model library / Axion1-0
Axion 1.0-35B is a 35.1 billion-parameter mixture-of-experts model aimed at scientific reasoning and tool-augmented problem solving.
Mixture of experts, count not published
Context length in tokens, as published
Weights at the released precision
Licence declared on the repository
Overview
Axion 1.0-35B is a 35.1 billion-parameter mixture-of-experts language model released by Axion1-0, intended for scientific reasoning, information seeking and tool-augmented problem solving.
The checkpoint contains 35.1 billion parameters and uses a Qwen3.5-moe architecture with an unspecified number of experts. It supports a context window of 262 144 tokens, allowing very long inputs. The model is distributed under the Apache 2.0 licence. Weight format is not published, and inference typically requires multiple GPUs with tensor parallelism to fit the model in memory.
Specification
| Repository | Axion1-0/Axion-1.0-35B |
| Publisher | Axion1-0 |
| Published | 16 October 2026 |
| Architecture | Qwen3_5MoeForConditionalGeneration |
| Model type | qwen3_5_moe |
| Parameters | 35,107,181,936 |
| 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 | 65.4 GiB | Full precision as released |
| fp8 | 32.7 GiB | 8-bit, near-lossless on most models |
| int4 | 16.3 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 | 11.0 GiB | about 144,281 tokens |
| RTX 5090 32GB | 4 | 39.8 GiB | the full 262,144 tokens |
| L40S 48GB | 2 | 16.0 GiB | about 209,817 tokens |
| A100 80GB | 1 | 4.1 GiB | about 53,841 tokens |
| H100 80GB | 1 | 4.1 GiB | about 53,841 tokens |
| RTX PRO 6000 96GB | 1 | 18.5 GiB | about 242,585 tokens |
| H200 141GB | 1 | 59.0 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 Axion1-0/Axion-1.0-35B \ --max-model-len 143360 \ --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 4 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 [email protected] with the model name and roughly what volume you expect.
Also in the library
Hemmingway-1.5 is a 26.9 B open-weight language model aimed at everyday writing tasks.
Nanbeige4.3-33B is a 33.1 billion-parameter Llama-style causal model released by Nanbeige for high-capacity agentic tasks and tea…
Nanbeige4.3-33B-Base is a 33.1-billion-parameter Llama-style causal language model released by Nanbeige under Apache-2.0.
Qwen3.8-27B-Longbook27-v2 is a 27-billion-parameter model fine-tuned for long-form text understanding and generation.
Hosting
Tell us the model and roughly what volume you expect. We reply with a price and an OpenAI-compatible endpoint, with your prompts never stored.