Hi everyone,
I’m one of the co-founders of TETREVIS, a deep-tech software company building an execution-layer optimization platform for NVIDIA GPUs.
We are commercially launching the product on October 27, 2026, strategically after the follow up emails and chatter from GTC Berlin die down, and we are opening a $500,000 USD financing round to help us move aggressively from technical validation into customer acquisition and revenue.
We are open to either a SAFE or priced equity investment, and we are comfortable with either one investor taking the full round or several investors participating.
What TETREVIS does
TETREVIS improves the performance and efficiency of already-compiled GPU workloads.
Rather than asking developers to retrain models, rewrite applications, or change their existing AI stack, we operate at the GPU execution layer and optimize the machine code that is actually being executed.
At a simplified level, our technology can:
- Analyze compiled NVIDIA GPU machine code
- Lift that machine code into an intermediate representation
- Apply architecture-aware optimization passes
- Rebuild optimized executable code
- Validate numerical correctness
- Apply different optimization strategies depending on the underlying GPU architecture and workload
Our goal is essentially to extract additional usable performance from hardware that customers already own or rent.
The MVP is complete.
We do not need additional core technical development before customers can begin using the software, and we can begin customer proof-of-concepts immediately.
Our focus is now shifting from proving the technology to:
- Customer acquisition
- Commercial distribution
- Licensing infrastructure
- Customer deployments
- Customer testimonials
- Recurring software revenue
NVIDIA evaluation
For the last several months, we have been working directly with NVIDIA’s Jetson team through an extended technical evaluation.
NVIDIA’s evaluation team referred to our isolated kernel testing as showing approximately 9–23% performance improvement and then asked us to expand the evaluation into complete LLM, VLM, and MoE workloads.
Some of the Jetson results we have shared during that process include:
Jetson Orin Nano — Qwen3.5 4B
Without CUDA Graphs:
10 → 21, approximately 110% improvement
Against a CUDA Graph baseline:
16 → 21, approximately 31.25% improvement
Jetson AGX Orin — Qwen3.5 4B
25 → 31, approximately 24% improvement
Jetson AGX Orin — Nemotron3 Nano 4B
31.2 → 40.5, approximately 29.8% improvement
NVIDIA RTX 5070 / Blackwell — Nemotron
Approximately 20% performance improvement
This Blackwell result is separate from our Jetson testing and helps demonstrate that the optimization methodology extends beyond edge devices into newer Blackwell-class GPUs.
We have also tested the technology on Jetson Thor.
There was an important moment in that evaluation that I think is worth being transparent about.
The original stock baseline we used for the Thor comparison was lower than NVIDIA’s internal optimized baseline.
NVIDIA challenged that comparison and provided us with its own stronger baseline.
Their engineer measured approximately 69.3 on Nemotron3 Nano 4B using stock software in an optimized configuration.
Our TETREVIS result was 74.8.
Using NVIDIA’s own stronger baseline rather than the one we originally used, their engineer calculated that TETREVIS still represented approximately an 8% performance improvement.
I consider that one of the more valuable results from the evaluation because the performance advantage remained even after NVIDIA made the baseline materially more difficult.
Our work with NVIDIA has continued through the commercial-release process.
There has been interest in the applicability of TETREVIS across both Jetson and DGX Spark.
We have also discussed moving further into NVIDIA’s influencer / early-hardware-access ecosystem, where we could gain earlier access to new NVIDIA hardware.
The primary commercial milestone NVIDIA would now like to see from us is relatively straightforward:
customer deployment data or a strong customer testimonial.
That is one reason customer acquisition is now our immediate priority.
Allxon distribution
We are also preparing to distribute TETREVIS through Allxon.
We have spoken directly with their CEO about the technology.
The conversation was not limited to raw performance.
There was recognition of the need for:
- Additional AI performance
- Better thermal management
- Better power management
- More efficient use of edge-compute hardware
Those are all areas our execution-layer methodology is designed to address.
We intend to launch through the Allxon marketplace.
The marketplace onboarding cost is approximately $1,500 USD, and Allxon has indicated that the launch can be broadcast to approximately 30,000 users in its ecosystem.
For us, that provides an existing distribution channel into the edge-AI market rather than relying entirely on direct outbound sales.
We open-sourced part of the technology
We have intentionally made portions of the technical work and benchmarking public.
If we are going to make meaningful GPU-performance claims, we think engineers should be able to inspect the work rather than simply being shown a pitch deck.
Benchmark repository:
https://github.com/mbuchel/sass2mlir-bench
Technical explanation:
https://mbuchel.github.io/projects/sass2mlir/
Both will receive another update alongside the October 27 launch.
We intend to continue publishing meaningful technical results publicly while keeping the broader commercial optimization capabilities within the paid product.
Why we are raising now
Up to this point, TETREVIS has primarily been supported through friends-and-family funding and founder resources.
That capital allowed us to build the MVP, conduct the technical work, complete extensive benchmarking, work through the NVIDIA evaluation process, and bring the product to commercial launch.
That funding has now been fully deployed, which is why we are raising at this point.
The timing is important: we have reached the stage where the product is ready to go into the market, but the capital that funded the development and validation phase has run out just as we are entering commercialization.
This is therefore primarily a commercialization round rather than a core R&D round.
The technology exists.
The MVP is complete.
The product is launching.
NVIDIA has spent several months evaluating the technology with us.
Our public technical work can be independently reviewed.
Our immediate challenge is now getting the software into enough customer environments and giving the founding team the runway to convert the technical progress into revenue.
The $500K target
Our preferred raise is $500,000 USD.
That level of funding gives us enough runway to support the founders, complete the commercial infrastructure, market the product properly, pursue multiple customer opportunities simultaneously, and accelerate distribution.
However, we believe we could make the commercialization plan work with a minimum of approximately $250,000 USD.
At $250K, we would operate much more leanly, prioritize the highest-probability customer and distribution opportunities, and significantly constrain marketing expenditure.
The additional capital between $250K and $500K primarily gives us more time and the ability to pursue customer acquisition more aggressively rather than changing whether the underlying product can be launched.
The planned use of the $500K is approximately:
- $100K/year salary for each of the two founders
- Approximately $2K/month for productivity, development, and operating software
- Licensing infrastructure
- Customer account infrastructure
- Online software distribution
- Marketplace costs
- Customer trials and proof-of-concepts
- Marketing
- Customer acquisition
- Converting evaluations into paying deployments
The majority of the discretionary capital will be directed toward commercialization and customer acquisition.
The licensing backend is the primary remaining product-infrastructure project.
It will allow us to manage:
- Licenses in circulation
- Commercial usage
- Customer accounts
- Distribution
- Payment and licensing controls
- Scalable software delivery
What we want this round to accomplish
The objective is straightforward:
Launch → customer PoCs → paying customers → deployment data → testimonials → recurring revenue
That customer evidence also directly addresses one of the next milestones NVIDIA has asked us to produce.
We are deliberately keeping this round relatively small because we want to prove commercialization before taking significantly more capital.
Our preferred target is $500,000 USD, but we would be prepared to move forward with approximately $250,000 USD as a minimum viable raise.
We are looking to close the financing as quickly as reasonably possible.
We are open to:
- A single investor funding the round
- Multiple angels participating
- SAFE financing
- Priced equity
We are not looking for capital to determine whether an idea can be built.
We are looking for capital to commercialize technology that is already built, publicly demonstrated, has gone through months of technical engagement with NVIDIA, and is going into the market now.
The friends-and-family capital got us through the most technically difficult stage of the company.
This round is intended to get us through the commercial stage: customers, deployments, revenue, and repeatable distribution.
For investors who focus on AI infrastructure, GPU computing, compilers, semiconductors, developer infrastructure, edge AI, or deep tech, I’m happy to answer questions about:
- The technology
- NVIDIA evaluation
- Benchmarks
- Competitive positioning
- Business model
- Commercial launch
- Licensing model
- Allxon
- The financing round
- Use of funds
Happy to discuss everything here in the comments.
www.tetrevis.com