Tencent
Hy3
前沿18.6K下载量871点赞Jul 2026发布日期262K tokens上下文Apache 2.0许可证91 卓越质量
Hy3 (295B parameters) requires approximately 186.3 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 21B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 215 GB of VRAM.
快速开始
— 复制粘贴即可本地运行Copy-paste commands to run Hy3 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "tencent/Hy3" \
--hf-file "Hy3-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters295B (21B active)
Architecturemoe (MoE)
Context262K tokens
Modalitytext
Min RAM115.1 GB
Rec. RAM180 GB (Q4_K_M)
LicenseApache 2.0
FamilyHunyuan
✓ Chat✓ Reasoning
About this model
- •295B total / 21B active per token — 192 experts, top-8 routed, plus 1 shared expert.
- •Rivals flagship open-source models with 2-5x more parameters.
- •256K context with strong multi-turn intent tracking (internal issue rate cut from 17.4% to 7.9%).
- •Apache 2.0 licensed.
- •Tencent recommends 8x H20-3e or similar high-memory GPUs for full-precision serving.
快速推荐
最佳硬件
Hy3 的最佳选择
运行此模型
量化选项
各量化级别的 VRAM 估算
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q1_0_G128 | 1.125 | 42.5 GB | Very Low | — |
Q2_0_G128 | 1.71 | 78.8 GB | Low | — |
Q2_K | 2 | 115.1 GB | Low | — |
Q3_K_S | 3 | 144.6 GB | Low | — |
NVFP4 | 4 | 165.2 GB | Medium | — |
Q4_K_M | 4 | 180.0 GB | Medium | — |
Q5_K_M | 5 | 212.4 GB | High | — |
Q6_K | 6 | 241.9 GB | High | — |
Q8_0 | 8 | 315.7 GB | Very High | — |
F16 | 16 | 604.8 GB | Maximum | — |
Quality benchmarks
Hy3 benchmark scores
Coding
SWE-bench Verified78.0%
HumanEval+—
Aider Polyglot—
LiveCodeBench—
Reasoning
MMLU-Pro—
GPQA Diamond90.4%
MATH-500—
ARC Challenge—
Source: official · 2026-07-02
硬件兼容性
全部硬件的适配估算
Computing compatibility...
内存详细分析
Reference: RTX 2060 6GB
Weights180.0 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom0.6 GB
常见问题
FAQ — Hy3
另请参阅