Inference optimization startup Infinity raises $15 million seed at $100 million valuation to run AI inference across heterogeneous chipsets.
Early-stage AI infrastructure research company Infinity Inc. raised $15 million in seed funding to develop software that automatically prepares new artificial intelligence chips to run inference workloads. The round values the company at $100 million on a post-money basis.
Infinity will use the capital to expand its engineering team, scale its automated research platform, and accelerate partnerships with chipmakers including d-Matrix Corp. The company is already generating millions of dollars in annual recurring revenue from chip design partnerships.
The company's core technology is Ignition, an AI agent that generates, tests, and optimizes the low-level software needed to run models efficiently on different processors. Infinity is targeting a longstanding obstacle for would-be NVIDIA competitors: even when their hardware is competitive, they lack a mature software ecosystem comparable to NVIDIA's CUDA. According to the company, its technology can help loosen NVIDIA's grip on the AI processor market by generating what is effectively a CUDA-level software stack in hours or days.
Ignition automates "the creation of debuggers, profilers, compilers, kernels, the orchestration of kernels in inference, and many other steps," said Founder and Chief Executive Jeremy Nixon.
Computing kernels are specialized software routines that perform core mathematical operations on AI chips. Traditionally, engineers must write and tune them for each combination of processor architecture and model, making it difficult for new hardware to keep pace with rapidly changing AI technology.
Infinity said human engineers determine what tools Ignition should create, while the agent handles implementation. To work with proprietary instruction sets, the company builds decompilers, which translate executable code back into high-level source code that can be manipulated using standard programming languages.
"Humans decide what tools the AI will build, such a memory layout compiler, while Ignition does the building," Nixon said.
Ignition increased inference throughput for the Qwen3-8B model by 34% compared with the widely used vLLM framework after one day of automated optimization. Nixon said the test used a single NVIDIA H100 graphics processor and has not yet been independently validated.
In a project with d-Matrix, Infinity's software achieved as much as 92% of the theoretical peak performance of d-Matrix's Corsair accelerator within 10 hours. It accomplished this by distributing matrix multiplication operations across all 32 computing units. Infinity said it had Qwen3, Qwen3.5, and Gemma4 running fully on the chip within 10 days.
Infinity tests generated software with tools that enforce required memory and numerical behavior. "We build 'bit-for-bit' accurate checking systems, memory clobber detectors and more," Nixon said.
The startup describes Ignition as a recursively self-improving system, meaning that it records successes and failures, builds tools to preserve successful techniques, and constructs a representation of previously solved problems to make subsequent optimization runs more efficient.
Infinity's revenue model links compensation to improvements it produces rather than software licensing fees. Optimization agreements generally give Infinity about 20% of the savings on additional computing purchases, calculated from gains in model throughput against an agreed baseline, according to Nixon.
The company was founded a year ago by Nixon, a former Google Brain researcher and co-founder of AGI House Labs Inc., a hybrid community hub, applied AI lab, and venture capital firm. Infinity said the new funding will also support discussions with additional chip companies.
Touring Capital LLC was a significant participant in the round, along with Principal Venture Partners LP and unnamed executives and angel investors from OpenAI, Anthropic, and chip makers.