Qwythos 9B Claude Mythos
By Empero AI · United States
Updated 2026-08-28
Overview
Qwythos 9B Claude Mythos is a community merge built on Qwen, tuned for roleplay and creative writing, with image input and a 1M-token context window — light enough for an 8GB GPU at Q4.
When to pick this model
- Long-form roleplay or interactive fiction needing huge context
- Creative writing and collaborative storytelling
- Lightweight local deployment on modest GPUs
- Basic image-grounded creative prompts
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 10 GB |
| FP16 (no quantization) | 18 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Qwythos 9B Claude Mythos fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 10 GB, and unquantized FP16 weights take 18 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwythos 9B Claude Mythos needs roughly 12 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 18 tokens/sec on entry-level GPUs, on the order of 28 tokens/sec on a mid-range card, and up to 45 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwythos 9B Claude Mythos to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwythos 9B Claude Mythos |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (10 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (18 GB used) |
| 32 GB | RTX 5090 | FP16 (18 GB used) |
Which GPU should you buy to run Qwythos 9B Claude Mythos?
To run Qwythos 9B Claude Mythos locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Lightweight — ~5GB VRAM at Q4, fits an 8GB GPU
- Extended 1M-token context
- Image input (multimodal)
- Apache 2.0, fully permissive
Limitations
- Community merge — quality less predictable than an official release
- Tuned for roleplay/creative use, weaker on factual tasks
- No Ollama tag — install from Hugging Face
Typical workloads
In our catalog grid, Qwythos 9B Claude Mythos is filed under Roleplay, Creative Writing, Image Analysis — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; vision-language work — screenshots, charts, scanned documents; multilingual workloads.
The 976k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense 9B transformer · Qwen-derived merge · 1M-token context window
Training: Community merge of Qwen-based models focused on roleplay and storytelling (themed 'Claude Mythos'). Training corpus details not published.
A lightweight, long-context merge built for roleplay and creative writing, not factual accuracy.
Quick start
# HuggingFace : empero-ai/Qwythos-9B-Claude-Mythos-5-1MOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwythos 9B Claude Mythos need?
At the recommended Q4_K_M quantization, Qwythos 9B Claude Mythos needs about 5 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 18 GB.
Can Qwythos 9B Claude Mythos run without a GPU?
Yes — with roughly 12 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwythos 9B Claude Mythos support?
Qwythos 9B Claude Mythos supports a 976k-token context window (1,000,000 tokens).
Can I use Qwythos 9B Claude Mythos commercially?
Yes. Qwythos 9B Claude Mythos is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Qwythos 9B Claude Mythos on consumer hardware?
Our compatibility engine estimates on the order of 28 tokens/sec on a mid-range GPU and up to 45 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwythos 9B Claude Mythos should I download first?
Start with Q4_K_M (5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.