Open-weight model
KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit
by Anar Zan AnarZan/KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit
KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit is an open-weight model from Anar Zan. 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.
Runs On
What it takes to serve KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit (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 23, 2026.
Model Card
The publisher has not written a card for this model.
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
- Model type
- llama
- Quantization
- bitsandbytes
Identity and Version
- Repository
- AnarZan/KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit
- Publisher
- Anar Zan
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 8B parameters
- Languages
- Not stated by the source
- Revision
- d5a57884a82663bc35c887bd206e2c11e06b195f
- First published
- 2026-09-29
- Last updated
- 2026-09-29
Files and Weights
7 files, 5.7 GB in total. The weights are 1 file totalling 5.7 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 5.7 GB | f0a685406926 |
| config.json | Configuration | 1.4 KB | — |
| generation_config.json | Configuration | 184 B | — |
| chat_template.jinja | Other | 2.0 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 17.2 MB | 6b9e4e7fb171 |
| tokenizer_config.json | Tokenizer | 335 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 5.7 GB
Released by Anar Zan through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 5.7 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 KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit
How much GPU memory does KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit 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 KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit 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 KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit's context length?
131,072 tokens, from the maximum position embeddings in its published configuration.