Model library / tencent
Hy-Memory-30B-A3B is a Tencent-developed long-term-memory writer model designed to replace LLMs in Mem0, MemOS and Hy-Memory frameworks.
Mixture of experts, count not published
Context length in tokens, as published
Weights at the released precision
Licence declared on the repository
Overview
Hy-Memory-30B-A3B is a compact long-term-memory writer model with about three billion active parameters, developed by Tencent Hunyuan. It is intended to replace the general-purpose language model inside Mem0, MemOS and Hy-Memory frameworks, handling decisions about what information to retain, how to represent it, and how new evidence updates persistent memory.
The model contains 30.1 billion total parameters and uses the HYV3ForCausalLM architecture with mixture-of-experts, though the expert count is not disclosed. It supports a context window of 262 144 tokens. Weights are released under the Apache-2.0 licence; the weight format is not published. Deployment can be done with vLLM (v0.31.0) via an OpenAI-compatible API.
Specification
| Repository | tencent/Hy-Memory-30B-A3B |
| Publisher | tencent |
| Published | 15 October 2026 |
| Architecture | HYV3ForCausalLM |
| Model type | hy_v3 |
| Parameters | 30,064,725,888 |
| Layers | 48 |
| Hidden size | 2,048 |
| Attention heads | 32, 4 key/value heads |
| Context length | 262,144 tokens |
| Vocabulary | 120,832 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 | 56.0 GiB | Full precision as released |
| fp8 | 28.0 GiB | 8-bit, near-lossless on most models |
| int4 | 14.0 GiB | 4-bit, smallest footprint |
The cache costs 96 KiB per token at 16-bit, so the full 262,144-token context of one request needs about 24.0 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 | 20.4 GiB | about 222,823 tokens |
| RTX 5090 32GB | 4 | 49.2 GiB | the full 262,144 tokens |
| L40S 48GB | 2 | 25.4 GiB | the full 262,144 tokens |
| A100 80GB | 1 | 13.5 GiB | about 147,456 tokens |
| H100 80GB | 1 | 13.5 GiB | about 147,456 tokens |
| RTX PRO 6000 96GB | 1 | 27.9 GiB | the full 262,144 tokens |
| H200 141GB | 1 | 68.4 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 tencent/Hy-Memory-30B-A3B \ --max-model-len 221184 \ --gpu-memory-utilization 0.90
Questions
The weights take about 56 GiB at the released precision. That fits on one A100 80GB, which leaves roughly 14 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
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.