Andromeda-8B-Coder.ggfu is an open-weight model from Anafiel Arce Lopez. It has 8B parameters and a 131,072-token context. At 16-bit it needs about 19.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
readme = """ basemodel: - meta-llama/Meta-Llama-3.1-8B - nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - NousResearch/Hermes-3-Llama-3.1-8B - code - llama-3.1 - merge - slerp - gguf - ollama Merge de Llama 3.1 8B + Nemotron + Hermes 3 usando SLERP + TIES.
Runs On
What it takes to serve Andromeda-8B-Coder.ggfu (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 | 16.1 GB | 19.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 8.0 GB | 9.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 4.0 GB | 4.8 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 18, 2026.
Andromeda-8B-Coder.ggfu on every accelerator the SAVRN Index prices, at every precision
Model Card
readme = """ basemodel: - meta-llama/Meta-Llama-3.1-8B - nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - NousResearch/Hermes-3-Llama-3.1-8B - code - llama-3.1 - merge - slerp - gguf - ollama Merge de Llama 3.1 8B + Nemotron + Hermes 3 usando SLERP + TIES. Optimizado para código, razonamiento y español. - Cuantizado a Q4KM para correr en cualquier laptop from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.frompretrained("fiel1986/Andromeda-8B-Coder", devicemap="auto") tokenizer = AutoTokenizer.frompretrained("fiel1986/Andromeda-8B-Coder") prompt = "Escribe una API en FastAPI con auth JWT" inputs = tokenizer(prompt, returntensors="pt").to("cuda")…
Excerpt from the card by Anafiel Arce Lopez.
Configuration
- Architecture
- LlamaForCausalLM
- Context length (tokens)
- 131,072
- Layers
- 32
- Hidden size
- 4,096
- Feed-forward size
- 14,336
- Attention heads
- 32
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 128,256
- RoPE base
- 500000
- Stored precision
- bfloat16
- Model type
- llama
Identity and Version
- Repository
- fiel1986/Andromeda-8B-Coder.ggfu
- Publisher
- Anafiel Arce Lopez
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 8B parameters
- Languages
- Not stated by the source
- Revision
- c50aa3e877afc56c0aa24863843cc6413148032e
- First published
- 2026-10-10
- Last updated
- 2026-10-10
Files and Weights
12 files, 16.1 GB in total. The weights are 4 files totalling 16.1 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00004.safetensors | Weights | 5.0 GB | 225a331768bb |
| model-00002-of-00004.safetensors | Weights | 5.0 GB | 3f9d9873d960 |
| model-00003-of-00004.safetensors | Weights | 4.9 GB | 61590043a43c |
| model-00004-of-00004.safetensors | Weights | 1.2 GB | 70c8a13b9d33 |
| config.json | Configuration | 956 B | — |
| generation_config.json | Configuration | 239 B | — |
| model.safetensors.index.json | Configuration | 23.9 KB | — |
| special_tokens_map.json | Configuration | 454 B | — |
| README.md | Documentation | 934 B | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 17.2 MB | 6b9e4e7fb171 |
| tokenizer_config.json | Tokenizer | 55.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 16.1 GB
Released by Anafiel Arce Lopez through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 16.1 GB |
| 16-bit | 16.1 GB |
| 8-bit | 8.0 GB |
| 4-bit | 4.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Andromeda-8B-Coder.ggfu
How much GPU memory does Andromeda-8B-Coder.ggfu need?
About 19.3 GB at 16-bit and 4.8 GB at 4-bit: the weights (8B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Andromeda-8B-Coder.ggfu 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.
What is Andromeda-8B-Coder.ggfu's context length?
131,072 tokens, from the maximum position embeddings in its published configuration.