Move from experiments to production-ready models sooner, with confidence.
A new performance baseline for LLM pre-training
Litespark is a high-performance LLM framework that speeds up training and inference while improving GPU efficiency.
Request a demoBenchmarks That Matter
Benchmark-proven acceleration
for LLM training and inference
H100:Benchmark GPUPyTorch
PyTorch with Litespark
0K
0K
Throughput (Tokens/sec/GPU)
Litespark reaches convergence faster on the same hardware, reducing
training time, energy consumption, and iteration cycles — without requiring
changes to existing models or workflows.
- 67kTokens/sec/GPU
- 83-86% lessEnergy use
- 6x fasterLLM training
- Zero-codePyTorch drop-in
- Months to daysIteration cycles
- 2x-6xMulti-node speedup
GPU hours per training run
10%Fewer training steps
Baseline Training
With Litespark
Reduced GPU hours through faster convergence
Lower cost per trained model.
Cuts training time and GPU hours, lowering infra and energy costs.
- Less GPU time
- Lower infra cost
Energy per training run
Baseline
337MWh256 GPUs
Litespark
133MWh0%256 GPUs
Reduced MWh and CO₂ emissions across GPU clusters
Lower energy per trained model.
Cuts MWh consumption and CO₂ emissions across 256–512 GPU clusters.
- Lower MWh
- Lower CO₂
Fits your existing ML pipeline
DataModel
TrainingLitesparkServing
ML AcceleratorLitesparkServingEfficient
Seamless
Scalable
SeamlessScalablePerformance without disruption.
Integrates seamlessly with NVIDIA and PyTorch — zero code changes.
- PyTorch-ready
- Native NVIDIA support
Zero-code DeploymentRun Zero-Code LLM
Run Zero-Code LLM
pre-training seamlessly on AWS
- Bring your own data or use open source data on HuggingFace.
- Model weights and checkpoints are stored safely in your secured environment.
- Track metrics and performance with Weights & Biases.
Your DatasetModelLitespark doesn't just train faster — it trains smarter, using fewer resources without sacrificing model quality. The result is measurable across every dimension of your infrastructure spend.
Your team ships better models sooner — and at a fraction of the energy and cost.
- Energy efficiency
Up to 83%Lower energy consumption
- Lower emissions
Up to 83%Lower CO₂ emissions
- GPU efficiency
Up to 6XHigher throughput per GPU
Litespark Benefits
Built for practical LLM training
Accelerate LLM training while maximizing GPU efficiency and reducing infrastructure overhead.
Reduce power consumption and operational overhead for AI workloads.
Consistent performance from single-node runs to large GPU clusters.
Edge / CPU Optimization
Performance gains
on every CPU platform
Litespark-Inference
Welcome to Mindbeam!
mindbeam@mbp ~ % litespark-inference chat
Loading model: bitnet-2b (microsoft/bitnet-b1.58-2B-4T-bf16, mode=neon)
Loading microsoft/bitnet-b1.58-2B-4T-bf16...
Loading cached ternary weights...
Loading tokenizer...
CLI Output
⎿ Litespark-Inference Chat
⎿ Type 'quit' or 'exit' to end the conversation ==
> You: What is the meaning of artificial intelligence?
>
Assistant: Artificial intelligence (AI) is a branch of computer science that aims to create systems capable of performing tasks that would typically require human intelligence.
Litespark-Inference unlocks high-performance execution on standard CPUs. A 2-billion parameter model runs efficiently in system RAM, delivering dramatically higher throughput and near-instant response times — without requiring GPUs or specialized accelerators.
By reducing memory footprint and accelerating time-to-first-token, inference becomes lightweight, responsive, and production-ready.
View on GitHub- 2x fasterApple Silicon M5
- 2x fasterAVX-512 Platforms
- 2x fasterIntel Core Ultra
Frequently Asked Questions
By shortening training time and improving GPU utilization, Litespark significantly lowers total GPU hours — resulting in major cost savings for large-scale training.
Accelerate LLM pre-training with Litespark.
Unlock higher throughput, lower energy use, and seamless integration with your existing stack.
Book a Demo