FrogNano 4B
Parameters
4B
Context Length
262K
Modality
Text
Architecture
Dense
KV Cache Rate
128 KB/tok
License
MIT License
Release Date
17 Sept 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
Hardware & System Requirements
Precision
Context
Workload Memory: 5.36 GB
INT4 · 8K context
Base Weights: 1.86 GB
KV Cache: 1.00 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
1x NVIDIA L4 24GB
5.4 / 21.6 GB usable
1x RTX 3060 12GB
5.4 / 10.8 GB usable
Apple M4 24GB
5.4 / 18.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Runtime requirements
Checkpoint format
FP16
CUDA
>= 11.8
PyTorch
>= 2.1
Transformers
>= 4.57.0.dev0
| Architecture | Precision | Attention kernel |
|---|---|---|
Blackwell, Hopper | Native FP16 | FlashAttention-3 |
Ada Lovelace, Ampere | Native FP16 | FlashAttention-2 |
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for FrogNano 4B available.
Rankings
Overall Rank
-
Coding Rank
-
About FrogNano 4B
FrogNano 4B is a compact multimodal language model developed by Microsoft for efficient edge computing and visual reasoning. It adapts dense transformer blocks to deliver strong multimodal performance under strict parameter budgets.
Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
16
Key-Value Heads
4
Attention Head Dimension
256
Position Embedding
ROPE
RoPE Theta
10,000,000
Linear Attention
Yes
Linear Attention Ratio
75.0%
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Base Precision
FP16
Hidden Dimension Size
2,560
Number of Layers
32
FFN Intermediate Size (Dense)
9,216
Multi-Token Prediction Heads
1
Tokenizer
Vocabulary Size
248,320
Resources
About FrogNano
The FrogNano model family developed by Microsoft.
Other FrogNano Models
- No related models available