FreeLLMAll models › tencent
Where to use it for free
- The lab's own chat: yuanbao.tencent.com (free with daily limits)
- Download and run it at home: official weights · GGUF files for Ollama and LM Studio
New: waiting for Arena votes
Released on 22 Oct 2026. Arena has not voted on this model yet. As soon as it does, the model moves into the ranking automatically, usually the next night. Until then you can still check its size, memory needs and speed below.
Speed on popular hardware
| Hardware | GPU memory | Fits? | Speed |
|---|---|---|---|
| RTX 3060 12 GB | 12 GB | Fits in GPU | 35 tok/s |
| RTX 4060 Ti 16 GB | 16 GB | Fits in GPU | 28 tok/s |
| RTX 3090 | 24 GB | Fits in GPU | 86 tok/s |
| RTX 4090 | 24 GB | Fits in GPU | 92 tok/s |
| RTX 5090 | 32 GB | Fits in GPU | 153 tok/s |
| RX 7900 XTX | 24 GB | Fits in GPU | 88 tok/s |
| 2 × RTX 3090 | 48 GB | Fits in GPU | 84 tok/s |
| 4 × RTX 3090 | 96 GB | Fits in GPU | 79 tok/s |
| Mac M4 Max 128 GB | 96 GB | Fits in GPU | 46 tok/s |
| Ryzen AI Max+ 395 128 GB | 96 GB | Fits in GPU | 25 tok/s |
| 8 × H100 SXM | 640 GB | Fits in GPU | 209 tok/s |
Estimate at 4-bit with 32K context, 64 GB of DDR5 for offload. Speed ≈ memory bandwidth × 78% ÷ data read per token, plus a fixed cost per token, calibrated on 44 real llama.cpp tests; typical error about ±10%. Compare all GPUs.
GPU memory needed
| Level | 8K context | 32K context | 128K context | Quality vs full |
|---|---|---|---|---|
| Q3_K_M | 4.2 GB | 7.8 GB | 22 GB | +10.5% noticeable loss |
| Q4_K_M | 4.7 GB | 8.3 GB | 23 GB | +2.8% small loss |
| Q5_K_M | 5.2 GB | 8.8 GB | 23 GB | +0.9% very small loss |
| Q6_K | 5.7 GB | 9.4 GB | 24 GB | +0.3% near-lossless |
| Q8_0 | 6.9 GB | 11 GB | 25 GB | ≈ 0% same as full |
| F16 | 11 GB | 15 GB | 30 GB | ≈ 0% same as full |
Weights + context cache (from the model's architecture) + runtime. Quality: increase in perplexity against the full model, from llama.cpp tests, scaled by model size. Simulate your own setup.
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