From scratch
The code, weights, training loops, and deployment engine are being built as first-party EMBER systems rather than borrowed black boxes.
Training-stage frontier model program
Building a from-scratch intelligence stack designed to reach serious coding and reasoning performance on local hardware.
EMBER stands for Efficient Mixture for Broad Emergent Reasoning. The goal is clear: train our own language model, our own tooling, and our own inference path from the ground up, then deliver the strongest possible capability per gigabyte of VRAM.
What EMBER is
A model program built for real engineering work.
EMBER is not a wrapper or a fine-tune. It is a full-stack language model effort covering tokenizer, architecture, training, evaluation, post-training, and inference. The design intent is to maximise coding and reasoning quality while remaining practical to serve on local hardware once training is complete.
Local-first target
The flagship design is oriented around delivering a large-model experience with a realistic path to 24 GB deployment and fast local inference.
Reasoning and code
EMBER is being tuned around the work that matters most in daily practice: coding, planning, analysis, tools, and long-session problem solving.
Architecture direction
The model is shaped around efficiency, bandwidth, and usable depth.
The current plan combines the strongest known efficiency levers into one coherent system: fine-grained experts, compressed attention, long-context support, quantization-aware training, and multi-token prediction for faster inference.
Mixture-of-experts
Targeting a large total parameter budget without paying dense-model cost on every token, so the model can stay ambitious without becoming unusably slow.
Latent attention
Compressing KV state to make long context viable and keep memory pressure under control for real local sessions.
Multi-token prediction
Using richer future-token supervision during training and a cleaner path to speculative decoding once the model is served.
Quantization-aware finish
Designing the final training stretch around the shipped inference format so local deployment does not take a major quality loss.
Funding position
Why EMBER is waiting on funding to complete the flagship run.
The project has moved into the training-stage stack build and validation phase, but the flagship model cannot be finished responsibly without a serious compute tranche. The current planning assumption is a run in the rough 500 H100 range, with supporting storage, data throughput, and large-scale reward verification.
Compute window
Funding is needed for a high-density H100 training allocation capable of supporting the flagship pretraining and post-training phases.
Data and storage
Scaling a serious model means finishing the dataset, checkpoint, and throughput layer with enough reliability to survive a large rented cluster.
Verification infrastructure
For a code-first model, rewardable execution and large parallel test environments are part of the product, not an optional extra.
Professional ask
Strategic funding and infrastructure partnerships are the unlock.
EMBER is positioned as a long-horizon intelligence asset, but the remaining step from training-stage validation to full completion is capital. If you fund compute, infrastructure, or the flagship run directly, you materially accelerate delivery.
Execution path
A staged program with clear progression and controlled risk.
Training stack validation
Finalise and verify the pipeline: data flow, trainer, evaluation harness, and local-serving direction.
Scaled trial model
Use a smaller model to validate the architecture choices, measure loss curves, and remove expensive uncertainty before the flagship run.
Flagship pretraining
Execute the full-scale run once the compute tranche is funded and the surrounding systems are ready to support it.
Post-training and deployment
Finish reward-based improvement, harden inference, and turn EMBER into a practical system rather than a research-only checkpoint.
Partner with the build
EMBER is ready for the next stage. Funding is the gate.
The vision is defined, the model direction is concrete, and the stack is moving through training-stage validation. The project now needs funding, compute access, or infrastructure backing to complete the flagship model.