chrono-2024-30000 is an open-weight model from Vadim Yakobchuk. It has 2B parameters and a 2,048-token context. At 16-bit it needs about 4.8 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 chrono-2024-30000 (2B 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 | 4.0 GB | 4.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 2.0 GB | 2.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 1.0 GB | 1.2 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 21, 2026.
chrono-2024-30000 on every accelerator the SAVRN Index prices, at every precision
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- NanochronoForCausalLM
- Context length (tokens)
- 2,048
- Layers
- 28
- Hidden size
- 1,792
- Feed-forward size
- 7,168
- Attention heads
- 14
- Key/value heads
- 14
- Vocabulary size
- 32,768
- Sliding window (tokens)
- 512
- RoPE base
- 100000
- Model type
- sn38-nanochrono
Identity and Version
- Repository
- vadimyakob/chrono-2024-30000
- Publisher
- Vadim Yakobchuk
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 2B parameters
- Languages
- Not stated by the source
- Revision
- 98f2c0a9f06dd73cf6462cb5e90ad285988a58ae
- First published
- 2026-10-12
- Last updated
- 2026-10-12
Files and Weights
8 files, 24.2 GB in total. The weights are 2 files totalling 24.2 GB in pt, safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 8.1 GB | 86743cd6467a |
| train_state.pt | Weights | 16.2 GB | 1145d23b382a |
| config.json | Configuration | 1.5 KB | — |
| generation_config.json | Configuration | 262 B | — |
| train_meta.txt | Other | 115 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 2.3 MB | — |
| tokenizer_config.json | Tokenizer | 294 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 24.2 GB
Released by Vadim Yakobchuk through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 24.2 GB |
| 16-bit | 4.0 GB |
| 8-bit | 2.0 GB |
| 4-bit | 1.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About chrono-2024-30000
How much GPU memory does chrono-2024-30000 need?
About 4.8 GB at 16-bit and 1.2 GB at 4-bit: the weights (2B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run chrono-2024-30000 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 chrono-2024-30000's context length?
2,048 tokens, from the maximum position embeddings in its published configuration.