Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and…
Ghastly2.0 is an open-weight model for text generation from Rose Loops. It has 14.8B parameters. At 16-bit it needs about 35.4 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Parameters14.8B
Context—
Weights—
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve Ghastly2.0 (14.8B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 29.5 GB | 35.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 14.8 GB | 17.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 7.4 GB | 8.9 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 23, 2026.
Ghastly2.0 on every accelerator the SAVRN Index prices, at every precision
Model Card
Identity and Version
- Repository
- RoseG/Ghastly2.0
- Publisher
- Rose Loops
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 14.8B parameters
- Languages
- Not stated by the source
- Revision
- f5fbb664d044cef79a6710b9722424e115584bc7
- First published
- 2026-10-20
- Last updated
- 2026-10-20
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
Download from Rose Loops
Released by Rose Loops through its official repository on Hugging Face.
Built From
- Derived from TheKloakedSignal/NONGHASTLY
Memory Requirements
| Precision | Weights in memory |
|---|---|
| 16-bit | 29.5 GB |
| 8-bit | 14.8 GB |
| 4-bit | 7.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Ghastly2.0
How much GPU memory does Ghastly2.0 need?
About 35.4 GB at 16-bit and 8.9 GB at 4-bit: the weights (14.8B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Ghastly2.0 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Similar Models
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and…
Purpose: Full-scale cognitive reasoning model with self-organizing memory and generative symbolic evolution SymbioticLM-14B is a 17.8-billion-parameter symbolic–transformer hybrid that couples high-capacity neural representation with structured symbolic cognition. It supports persistent memory, entropic recall, multi-stage symbolic routing, and self-organizing knowledge structures. This is an experimental research checkpoint — the capability claims below describe architectural intent, not benchmarked results (see Limitations). This model is ideal for advanced reasoning agents, research assistants, and symbolic math/code generation systems. - Long-form symbolic theorem generation and proof…
People's InvoiceLM-14B is a domain-specific fine-tuned model built on Qwen3-14B base model, optimised for template-free invoice understanding, structured data extraction, and real-world finance document workflows. It is designed for the messy reality of enterprise invoices: changing supplier formats, inconsistent field layouts, scanned documents, and operational data that must be reviewed before it reaches downstream systems. People's InvoiceLM powers invoice intelligence capabilities within E-Flow, Effectz.AI's document execution platform. People's LM is Effectz.AI's family of compact, specialised language models built for real-world business workflows in emerging markets — built for the…
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more…
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and…