Run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF locally
empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF is a large language model with 8.95 billion parameters, built on the qwen35 architecture. It is released under the apache-2.0 license and has been downloaded 907,682 times.
To run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF locally at a 4,096-token context, its quantized versions need between 2.98 GB (F16, lowest quality) and 35.11 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is F16, needing about 2.98 GB. That means empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| F16 | 1.64 | Very low | 1.71 GB | 0.47 GB | 2.98 GB | 233.9 t/s | Fits in VRAM |
| Q4_K_M | 10.29 | Excellent | 10.73 GB | 0.47 GB | 12.0 GB | 4.7 t/s | Offload |
| Q5_K_M | 11.79 | Excellent | 12.29 GB | 0.47 GB | 13.56 GB | 4.1 t/s | Offload |
| Q6_K | 13.38 | Excellent | 13.95 GB | 0.47 GB | 15.22 GB | 3.6 t/s | Offload |
| Q8_0 | 17.26 | Excellent | 17.99 GB | 0.47 GB | 19.26 GB | 2.8 t/s | Offload |
| BF16 | 32.46 | Excellent | 33.83 GB | 0.47 GB | 35.11 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF?
You need about 2.98 GB of VRAM to run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF entirely on the GPU using the F16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF fully on the GPU using F16 (about 2.98 GB).
Can I run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF fully on the GPU using Q6_K (about 15.22 GB).
Can I run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF fully on the GPU using Q8_0 (about 19.26 GB).
What is the best quantization for empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.