chrono-2025-a-18000 is an open-weight model from Vadim Yakobchuk. It has 1.7B parameters and a 32,768-token context. At 16-bit it needs about 4.1 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-2025-a-18000 (1.7B 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 | 3.4 GB | 4.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 1.7 GB | 2.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.9 GB | 1.0 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-2025-a-18000 on every accelerator the SAVRN Index prices, at every precision
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- Qwen3ForCausalLM
- Context length (tokens)
- 32,768
- Layers
- 28
- Hidden size
- 2,048
- Feed-forward size
- 6,144
- Attention heads
- 16
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 151,936
- Model type
- qwen3
Identity and Version
- Repository
- vadimyakob/chrono-2025-a-18000
- Publisher
- Vadim Yakobchuk
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 1.7B parameters
- Languages
- Not stated by the source
- Revision
- fdc4f4bbddf31b43eb0c757de6af977908027b90
- First published
- 2026-10-15
- Last updated
- 2026-10-15
Files and Weights
12 files, 10.3 GB in total. The weights are 5 files totalling 10.3 GB in bin, pt, pth, safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 3.4 GB | 79dcb19f3751 |
| optimizer.pt | Weights | 6.9 GB | 0d4d5bdc4b2c |
| rng_state.pth | Weights | 14.6 KB | 61c19bab1174 |
| scheduler.pt | Weights | 1.5 KB | 24f4d417f914 |
| training_args.bin | Weights | 5.2 KB | ae3a3514b302 |
| config.json | Configuration | 1.4 KB | — |
| generation_config.json | Configuration | 165 B | — |
| trainer_state.json | Configuration | 63.5 KB | — |
| chat_template.jinja | Other | 4.1 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 11.4 MB | be75606093db |
| tokenizer_config.json | Tokenizer | 697 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 10.3 GB
Released by Vadim Yakobchuk through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 10.3 GB |
| 16-bit | 3.4 GB |
| 8-bit | 1.7 GB |
| 4-bit | 0.9 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About chrono-2025-a-18000
How much GPU memory does chrono-2025-a-18000 need?
About 4.1 GB at 16-bit and 1 GB at 4-bit: the weights (1.7B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run chrono-2025-a-18000 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-2025-a-18000's context length?
32,768 tokens, from the maximum position embeddings in its published configuration.