Model reference · open weights
Supra1.5-exp is an open-weight language model from SupraLabs. Supra1.5-50M-Base-exp (FP32) weighs 104 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the SupraLabs/Supra1.5-50M-Base-exp model card, 2026-10-01
What it is
| Released by | SupraLabs |
|---|---|
| Released | 2026-06-10 |
| Parameters | 52M |
| VRAM | 104 MB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 5K each | |||
| RTX 3060 12 GB | 175 | all 5K | 11.6 GB |
| RTX 4060 Ti 16 GB | 235 | all 5K | 15.4 GB |
| RTX 3090 24 GB | 362 | all 5K | 23.4 GB |
| RTX 4090 24 GB | 361 | all 5K | 23.4 GB |
| RTX 5090 32 GB | 483 | all 5K | 31.0 GB |
| L40S 48 GB | 689 | all 5K | 44.0 GB |
| A100 80 GB | 1000+ | all 5K | 78.2 GB |
| H100 80 GB | 1000+ | all 5K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 1000+ | all 5K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 1000+ | all 5K | 107 GB |
| H200 141 GB | 1000+ | all 5K | 138 GB |
| B200 180 GB | 1000+ | all 5K | 176 GB |
| Requests at once | 5K tokens each |
|---|---|
| 1 | 649 MB |
| 5 | 900 MB |
| 8 | 1.1 GB |
| 16 | 1.6 GB |
| 32 | 2.6 GB |
| 64 | 4.6 GB |
One card, with vLLM's small-card settings.
From the model card
Continued Pretraining • 50M Parameters • 5K Context
Supra-1.5-50M-Base-exp is a continued-pretrained 50M parameter Llama-style base
model derived from SupraLabs/Supra-50M-Base. The target update expands the
usable context window from 1,024 tokens to 5,120 tokens using RoPE scaling and
full-weight continued pretraining.
The model keeps the original Supra-50M architecture and tokenizer:
| Specification | Value |
|---|---|
| Architecture | LlamaForCausalLM |
| Parameters | ~50M |
| Vocabulary Size | 32,000 |
| Hidden Size | 512 |
| Layers | 12 |
| Attention Heads | 8 |
| KV Heads | 4 |
| Context Length | 5,120 tokens |
| Tokenizer | Original Supra byte-level BPE tokenizer |
This is CPT, not instruction fine-tuning. Training uses packed raw text with standard causal language-modeling loss:
labels = input_idsThe current local training mix prepared for this run is:
Supervised Fine-Tuning (SFT) and Reinforcement Learning
Credit goes to @QyrouNnet-AI
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.