A flagship 295B-parameter model can now run on a single 96GB inference GPU, with decoding speed increased by 50%.

Tencent Hunyuan team releases quantized versions of Hy3 (295B parameters). The 1-bit version (IQ1_M) compresses weights from 598GB to 85.5GB, reducing size by 6.7x, deployable on a single 96GB inference GPU; the 4-bit version (Q4_K_M) uses 169.9GB, fits on two GPUs, with performance close to the full-precision model. With MTP speculative decoding, decoding speed increases by ~50% for the 1-bit version and nearly 60% for the 4-bit version.

The significance goes beyond “being cheaper.” Previously, inference for models at the 295B parameter level often required multiple machines and GPUs, affordable only by large cloud providers and top labs. Now running on a single 96GB GPU means more small and medium institutions and independent researchers can access this capability locally, owning and controlling model weights directly instead of renting via APIs.

On tasks like agent, multilingual code, tool calling, and long-context understanding, the quantized versions perform close to the full-precision model. All versions are open-source in GGUF format, compatible with the llama.cpp ecosystem.

The core trend in large model quantization is no longer “acceptable performance loss,” but rather “performance loss itself is increasingly smaller.” When a 1-bit version can retain near full-precision capability, quantization is no longer a compromise but another deployment strategy.

Now, whether the smallest deployment threshold can be achieved under equal capability has become the most important competitive dimension for large model deployment.

Hy3 model: https://huggingface.co/tencent/Hy3 Low-bit GGUF model: https://huggingface.co/AngelSlim/Hy3-GGUF… GPTQ Int4 model: https://huggingface.co/AngelSlim/Hy3-GPTQ-Int4…

tencent/Hy3 · Hugging Face

Source: https://huggingface.co/tencent/Hy3 中文 (https://huggingface.co/tencent/Hy3/blob/main/README_CN.md)| English

License (https://huggingface.co/tencent/Hy3#license)HuggingFace (https://huggingface.co/tencent/Hy3)ModelScope (https://modelscope.cn/models/Tencent-Hunyuan/Hy3)cnb.cool (https://cnb.cool/ai-models/tencent/Hy3)GitCode (https://ai.gitcode.com/tencent_hunyuan/Hy3)

🖥️Official Website (https://aistudio.tencent.com/)| 💬GitHub (https://github.com/Tencent-Hunyuan/Hy3)

https://huggingface.co/tencent/Hy3#table-of-contentsTable of Contents

• Model Introduction (https://huggingface.co/tencent/Hy3#model-introduction)
• Stronger Agent Capabilities (https://huggingface.co/tencent/Hy3#stronger-agent-capabilities)
• More Reliable Product Experiences (https://huggingface.co/tencent/Hy3#more-reliable-product-experiences)
• Benchmark Appendix (https://huggingface.co/tencent/Hy3#benchmark-appendix)
• News (https://huggingface.co/tencent/Hy3#news)
• Model Links (https://huggingface.co/tencent/Hy3#model-links)
• Quickstart (https://huggingface.co/tencent/Hy3#quickstart)
• Deployment (https://huggingface.co/tencent/Hy3#deployment)- vLLM (https://huggingface.co/tencent/Hy3#vllm) - SGLang (https://huggingface.co/tencent/Hy3#sglang)
• Finetuning (https://huggingface.co/tencent/Hy3#finetuning)
• RL Post-training (https://huggingface.co/tencent/Hy3#rl-post-training)
• Quantization (https://huggingface.co/tencent/Hy3#quantization)
• License (https://huggingface.co/tencent/Hy3#license)
• Contact Us (https://huggingface.co/tencent/Hy3#contact-us)

https://huggingface.co/tencent/Hy3#model-introductionModel Introduction

Hy3is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks.

PropertyValueArchitectureMixture-of-Experts (MoE)Total Parameters295BActivated Parameters21BMTP Layer Parameters3.8BNumber of Layers (excluding MTP layer)80Number of MTP Layers1Attention Heads64 (GQA, 8 KV heads, head dim 128)Hidden Size4096Intermediate Size13312Context Length256KVocabulary Size120832Number of Experts192 experts, top-8 activatedSupported PrecisionsBF16

https://huggingface.co/tencent/Hy3#stronger-agent-capabilitiesStronger Agent Capabilities

Building on Hy3 Preview, we further improved the quality and diversity of post-training data while scaling up RL training. Hy3 shows solid gains across reasoning, agentic, and long-context tasks, competitive with much larger flagship models.

In productivity scenarios such as coding, office work, financial modeling, frontend design, and game development, Hy3 has made remarkable progress and can now serve as a reliable, cost-effective model option.

We don’t think public benchmark scores tell the full story. So we ran a blind evaluation with 270 experts using tasks from their work, and Hy3 scored 2.67/4, outperforming GLM-5.1 at 2.51/4. The advantage was most substantial in frontend development, data & storage, and CI/CD tasks.

https://huggingface.co/tencent/Hy3#more-reliable-product-experiencesMore Reliable Product Experiences

Model usefulness is not fully captured by benchmarks. Based on extensive product feedback, we identified and fixed the following issues, receiving consistently positive feedback from product teams.

Stability of tool calls and output formats: We fixed multiple baseline reliability issues, bringing the model to production-grade standards across tool configurations and output constraints. Tool-call error recovery and overall efficiency improved. Hy3 also generalizes across different agent scaffoldings. On SWE-Bench Verified, accuracy variance across scaffoldings like CodeBuddy, Cline, and KiloCode remains within 4%.

Knowledge and anti-hallucination: Guided by the ideal of “answer when grounded, state when evidence is missing, do not conflate sources or fabricate data,” we implemented fine-grained data cleaning and training constraints. In internal evaluations based on real-world scenarios, Hy3’s hallucination rate dropped from 12.5% to 5.4%, and commonsense error rates fell from 25.4% to 12.7%. These improvements materially reduce fact conflation, fabrication, and logical contradiction.

Complex context retention and multi-turn intent tracking: Through joint optimization of SFT and RL, Hy3 improved on operational pain points like coreference resolution, ellipsis recovery, and multi-turn constraint inheritance. On internal comprehensive multi-turn tests, the issue rate dropped from 17.4% to 7.9%. Hy3 also improved markedly on long-dialogue evals like MRCR. Its outputs are more concise while ensuring complex intents do not decay or drift over long-horizon interactions.

https://huggingface.co/tencent/Hy3#benchmark-appendixBenchmark Appendix

https://huggingface.co/tencent/Hy3#newsNews

• 🔥 We open-sourceHy3andHy3-FP8model weights onHugging Face (https://huggingface.co/tencent/Hy3),ModelScope (https://modelscope.cn/models/Tencent-Hunyuan/Hy3),GitCode (https://ai.gitcode.com/tencent_hunyuan/Hy3), andCNB (https://cnb.cool/ai-models/tencent/Hy3).

https://huggingface.co/tencent/Hy3#model-linksModel Links

https://huggingface.co/tencent/Hy3#quickstartQuickstart

Deploy Hy3 withvLLM (https://huggingface.co/tencent/Hy3#vllm)orSGLang (https://huggingface.co/tencent/Hy3#sglang)first, then call the OpenAI-compatible API:

`` from openai import OpenAI

client = OpenAI(base_url=“http://127.0.0.1:8000/v1”, api_key=“EMPTY”)

response = client.chat.completions.create( model=“hy3”, messages=[ {“role”: “user”, “content”: “Hello! Can you briefly introduce yourself?”}, ], temperature=0.9, top_p=1.0, # reasoning_effort: “no_think” (default, direct response), “low”, “high” (deep chain-of-thought) extra_body={“chat_template_kwargs”: {“reasoning_effort”: “no_think”}}, ) print(response.choices[0].message.content) ``

Recommended parameters:temperature=0\.9,top\_p=1\.0. Reasoning mode: Setreasoning\_effortto"high"for complex tasks (math, coding, reasoning) or"no\_think"for direct responses.

See theDeployment (https://huggingface.co/tencent/Hy3#deployment)section below for how to start the API server.

https://huggingface.co/tencent/Hy3#deploymentDeployment

Hy3 has 295B parameters in total. To serve it on 8 GPUs, we recommend using H20-3e or other GPUs with larger memory capacity.

For production serving, we recommend using vLLM or SGLang, both of which provide dedicated recipes for Hy3:

• vLLM (https://github.com/vllm-project/vllm)- seevLLM recipes (https://recipes.vllm.ai/tencent/Hy3)
• SGLang (https://docs.sglang.io/)- seeSGLang cookbook (https://lmsysorg.mintlify.app/cookbook/autoregressive/Tencent/Hy3)

https://huggingface.co/tencent/Hy3#vllmvLLM

Build vLLM from source:

uv venv --python 3.12 --seed --managed-python source .venv/bin/activate git clone https://github.com/vllm-project/vllm.git cd vllm uv pip install --editable . --torch-backend=auto

Start the vLLM server with MTP enabled:

``

Switch to trtllm backend to work-around mnnvl workspace size issue.

export VLLM_FLASHINFER_ALLREDUCE_BACKEND=trtllm vllm serve tencent/Hy3
–tensor-parallel-size 8
–speculative-config.method mtp
–speculative-config.num_speculative_tokens 2
–tool-call-parser hy_v3
–reasoning-parser hy_v3
–enable-auto-tool-choice
–port 8000
–served-model-name hy3 ``

https://huggingface.co/tencent/Hy3#sglangSGLang

Build SGLang from source:

git clone https://github.com/sgl-project/sglang cd sglang pip3 install pip --upgrade pip3 install "transformers>=5.6.0" pip3 install -e "python"

Launch SGLang server with MTP enabled:

python3 -m sglang.launch_server \ --model tencent/Hy3 \ --tp-size 8 \ --tool-call-parser hunyuan \ --reasoning-parser hunyuan \ --speculative-num-steps 2 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 3 \ --speculative-algorithm EAGLE \ --port 8000 \ --served-model-name hy3

https://huggingface.co/tencent/Hy3#finetuningFinetuning

Hy3 provides a complete model finetuning pipeline. For detailed documentation, please refer to:Finetuning Guide (https://huggingface.co/tencent/Hy3/blob/main/finetune/README.md)

https://huggingface.co/tencent/Hy3#rl-post-trainingRL Post-training

Hy3 supports GRPO reinforcement learning training withverl (https://github.com/volcengine/verl), training on Megatron-LM (model conversion via NVIDIA Megatron-Bridge) with vLLM rollout. For detailed documentation, please refer to:RL Training Guide (https://huggingface.co/tencent/Hy3/blob/main/rl/README.md)

https://huggingface.co/tencent/Hy3#quantizationQuantization

We provideAngelSlim (https://github.com/tencent/AngelSlim), a more accessible, comprehensive, and efficient toolkit for large model compression. AngelSlim supports a comprehensive suite of compression tools for large-scale multimodal models, including common quantization algorithms, low-bit quantization, and speculative sampling.

https://huggingface.co/tencent/Hy3#licenseLicense

Hy3 is released under theApache License 2.0. SeeLICENSE (https://huggingface.co/tencent/Hy3/blob/main/LICENSE)for details.

https://huggingface.co/tencent/Hy3#contact-usContact Us

If you would like to leave a message for our R&D and product teams, welcome to contact us. You can also reach us via email:

📧[email protected]

Hy3 is developed by the Tencent Hy Team.