Startup profile · funding coverage
NeuralMagic funding, valuation and investors
Software to run deep learning inference efficiently on commodity CPUs.
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Funding, valuation & investors
Answer-first snapshotLatest funding
Series A
$30M · October 2021
Latest known valuation
Not publicly disclosed
Total disclosed equity funding
$45M
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
series a · Somerville, MA
Overview
Neural Magic develops software that optimizes deep learning model inference on commodity CPUs using sparsity and quantization techniques — delivering GPU-class performance without specialized hardware. MIT spinout backed by a16z in seed and NEA-led $30M Series A in October 2021.
Why NeuralMagic is interesting
MIT spinout making GPU-class inference on CPUs via sparsity — a16z backed seed; NEA-led $30M Series A as cost-sensitive AI deployment sought CPU alternatives.
Product & use cases
Neural Magic's SparseML and DeepSparse engine optimize and run neural networks on CPUs by exploiting weight sparsity — reducing compute and memory requirements for inference workloads in cost-sensitive deployments.
- CPU-based ML inference without GPU hardware
- Edge and on-prem inference cost reduction
- Sparsified model deployment for production inference
Key facts
- Seed (Nov 2019): $15M with a16z — TechCrunch
- Series A (Oct 2021): $30M led by NEA — PRWeb
- MIT spinout focused on CPU inference sparsity
Funding history (newest first)
Series A
2021-10 $30MSource: https://www.prweb.com/releases/neural-magic-series-a
Seed
2019-11 $15MInvestors in our directory
Funds linked from NeuralMagic's profile — open a fund page for stage focus and related deal articles.
Competitive landscape
Edge: Sparsity-based optimization unlocks CPU inference performance — valuable where GPU supply is constrained or edge deployment prohibits accelerators.
CPU inference is niche as GPUs dominate training and most inference. Neural Magic targets cost-sensitive and edge deployments where GPU economics fail. Intel and open-source ONNX Runtime compete on CPU optimization.
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Intel OpenVINO incumbent
Intel CPU inference optimization.
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ONNX Runtime alternative
Open-source inference engine.
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NVIDIA TensorRT incumbent
GPU inference optimization.
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Qualcomm AI Engine adjacent
Mobile/edge AI inference.
Notable stories
- Neural Magic originated from MIT research on weight sparsity in neural networks — enabling models to run efficiently on CPUs by skipping zero-weight computations.
Industries
FAQs about NeuralMagic
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