AI models hoard redundant knowledge — noise, biases, overfitted patterns. Remap Studios surgically removes unnecessary neurons, shrinks the architecture, and retrains a leaner model in a fraction of the compute.
0+
Models unlearned
across research labs and companies
0.0%
Avg compute reduction
without accuracy loss
0.0M
GPU hours saved
equivalent to $6.3M in compute
0.0%
Avg accuracy retained
after selective unlearning
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Supported models
HuggingFace compatible
0.0K
Developers
using Remap Studios
Select a model, drag the slider, and watch the metrics update in real time. Every number reflects real benchmarks from our test suite.
13.5 GB
7B parameters
9.3 GB
50% smaller
9.3 GB
↓ 50% reduction
$2,436
↓ 42% savings
3d
vs 6 weeks original
2.3×
faster inference
Click any layer in the diagram to see what it does, how many parameters it has, and whether it's a primary target for unlearning.
Hover over nodes to inspect them. Toggle between the original bloated network and the unlearned, optimized version.
All nodes erased. Toggle to rebuild.
Click through each stage to understand the complete pipeline. Every step has real metrics from production runs.
Load any HuggingFace-compatible model in .safetensors or .pt format. The system analyzes architecture, counts parameters, and maps every tensor.
Four steps from a bloated, expensive model to a lean, fast one. Every step is automated — you just point at your model.
Map every node, weight, and connection in the neural network. Identify which neurons are essential and which are redundant, noisy, or overfitted.
Our algorithm detects dead neurons, overlapping representations, spurious correlations, and knowledge that no longer serves the model's purpose.
Surgically remove the identified nodes and connections. The network architecture shrinks while preserving core functionality.
The shrunken model retrains in a fraction of the original time. Less nodes = less compute = same accuracy, dramatically lower cost.
This is a simplified neural network. Click hidden-layer nodes to erase them and watch the compute savings in real time. Use Auto-Erase for bulk removal.
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Click any hidden-layer node to erase it. Use Auto-Erase to bulk-remove redundant nodes.
Verified benchmarks across popular open-source models. Any HuggingFace-compatible causal LM is supported.
Meta
7B / 13B / 70B
38–52%
99.1%
2.3×
Mistral AI
7B
41–48%
98.8%
2.1×
Meta
7B / 13B / 34B
35–55%
99.3%
2.5×
TII
7B / 40B
32–47%
98.5%
1.9×
Microsoft
2.7B
44–60%
99.0%
2.8×
2B / 7B
36–50%
98.9%
2.2×
Alibaba
0.5B–72B
30–58%
98.7%
2.0×
DeepSeek
7B / 67B
33–49%
98.6%
2.1×
Input your model size, training frequency, and GPU costs. See exactly how much you'll save annually.
$5,443
95% reduction in compute costs
$113
per training run
2,177
hours of GPU time
$454
per month
1089 kg
CO₂ equivalent saved
Based on 4 training runs/month with 48 GPU hours each
Fine-grained calculator for compute units, training steps, and cost per cycle.
98%
By removing 35% of nodes and retraining for just 3 epochs instead of 100.
$250.00
Original training cost
$4.88
Retraining cost
$245.13
per training cycle · Model shrunk to 65 nodes
Remap Studios is in early access. Download the desktop app or join the waitlist for API access.