Fine-tune and serve open-weight models on your own cloud GPUs without managing ML infrastructure
Everything has an algorithm
Stage
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Founded
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Based in
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Pricing model
Freemium
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Pricing
Free: $0 (1 training run per month, 1 model deployment, 1 member); Starter: $49/month (5 training runs per month, 5 model deployments, 1 member); Pro: $199/month (unlimited training runs, 1,000 model deployments, 10 included members, $30/month for each extra seat); Enterprise: custom pricing. Free i
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Key features
Train models with SFT, DPO, or RL using LoRA or QLoRA
Monitor training with real-time loss, time and cost tracking
Evaluate models by comparing base and tuned performance
Serve models as OpenAI-compatible API or wake-on-demand
Support for vision models including Gemma 4 and Qwen3.8
DPO preference learning from paired answers
Run budget-controlled experiments with hour and price limits
Download adapters as standard LoRA files or publish to Hugging Face
Constant-state attention: models serve any context length from the same memory per request
HCGM: models run from disk storage instead of memory
M-Rail: context processed by a model can be reused instantly in future requests
CPUDA/sGPU: run unmodified CUDA programs on CPUs with bit-identical results
Organ grafting: transfer capabilities between models without training
Personal instruction set (ISA) compiled on device from phone signals
Population ISA: compile laws from many people without personal records
Digital twins of customers, fleets, cities or markets
Results identical across all machines and architectures
Computation cost follows novelty not data size
What makes it different
No training scripts required; workflow runs in console, CLI or via AI agent
GPU costs billed by your provider with zero markup from Tensorant
Data stays in your own storage; Tensorant keeps only workflow records
Free plan covers complete workflow on one model from import to API
Models are downloadable and portable to other platforms
Constant memory cost regardless of context length or data size
Exact reuse: every computed result is kept and reused instantly
Portable execution with bit-identical results across all hardware
Models run from storage without retraining or fitting in memory
Privacy-preserving: raw data never leaves the device
Tensorant
Wirkung