Infinity Ignition
by Infinity Inc.
AI research agent that writes and optimizes inference kernels to make any AI chip production-ready in days
Infinity Ignition is an autonomous AI research agent that generates, tests, debugs and optimizes the low-level compute kernels determining how efficiently a chip runs AI models, compressing inference software development from years into days. It is aimed at silicon vendors, cloud providers and infrastructure teams deploying models on non-NVIDIA accelerators.
At a Glance
- Category
- Infrastructure & Cloud
- Pricing
- Usage-based, Contact for pricing
- Target Market
- CTOs, Heads of AI Infrastructure, Silicon Vendors, Platform Engineering Leaders, ML Systems Engineers
- Founded
- 2025
Key Features
- ✓Autonomous kernel generation
Ignition writes the low-level compute kernels that determine how efficiently a chip runs AI models, without manual engineering.
- ✓Automated test, debug and rewrite loop
The agent measures hardware performance against generated code and automatically rewrites it when results fall short.
- ✓Full toolchain synthesis
Generates debuggers, profilers, compilers, kernels and the orchestration of kernels used in inference.
- ✓Bit-for-bit correctness verification
Builds decompilers and bit-for-bit accuracy checking plus memory detection tooling to validate generated code.
- ✓Self-improving research loop
Records successful optimizations and constructs reusable problem representations that improve subsequent runs.
- ✓Outcome-based commercial model
No upfront license fee — Infinity takes roughly 20% of the compute cost savings delivered, measured in tokens per second.
Capabilities
Use Cases
- •New accelerator bring-up
Reached 92% of theoretical peak performance on d-Matrix's Corsair accelerator within 10 hours.
- •Throughput optimization on existing hardware
Delivered a 34% inference throughput increase for Qwen3-8B versus the vLLM framework in a single day.
- •Rapid model porting to new silicon
Deployed Qwen3, Qwen3.5 and Gemma4 fully on chip within 10 days.
Ideal For
Best For
- ✓Silicon vendors bringing a new AI accelerator to production-ready inference
- ✓Infrastructure teams reducing inference cost by running models on non-NVIDIA chips
- ✓Extracting higher throughput from existing accelerator fleets without manual kernel engineering
Deployment
Market Analysis
Pros
- ✓Directly attacks the software bottleneck that keeps non-NVIDIA silicon out of production
- ✓Outcome-based pricing removes upfront risk for buyers
- ✓Concrete published benchmarks against vLLM and on d-Matrix hardware
- ✓Already generating millions in ARR from chip design partnerships
Cons
- ✗Very early stage — founded 2025 with 26 employees at the time of its seed round
- ✗Only one publicly confirmed customer (d-Matrix)
- ✗Sells primarily to silicon vendors, so most enterprises benefit indirectly
- ✗No public product documentation, SDK or self-serve access
Pricing
Outcome-based partnership
Contact for pricing
- ✓No upfront license fee
- ✓Approximately 20% of compute purchase savings from throughput gains
- ✓Savings measured in tokens per second
- ✓Chip design partnership engagement
Infinity charges no upfront license fees and instead takes a percentage cut of the performance gains and cost savings it delivers, reported at roughly 20% of the customer's compute purchase savings, measured in tokens per second.
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