Blazing fast inference. Hardware portability. Full stack control.
Customize performance down to the kernel, while deploying seamlessly across GPU vendors without code rewrites.
Full stack control
Control execution from models to kernels, with clear performance visibility.
Full customization
Use fine-tuned weights and custom models without workflow changes.
Deep Observability
Low-level telemetry reveals bottlenecks and optimization opportunities.
Portability across hardware
Run on hardware that optimizes price, availability, and performance.
Modular vs. Alternatives
Self Managed
Too much expertise required
Expensive MLOps team
Custom optimizations require CUDA experts ($200k-300k/year)
NVIDIA-only code limits hardware flexibility
compute contract nightmares
The sweet spot

Customize your performance easily, down to the kernel
Scale seamlessly without an MLOps team
Same code runs on NVIDIA + AMD
Deep observability to know what’s working
Inference Endpoints
Cookie cutter offering (no control):
Black-box optimizations you can't customize
Waiting on vendor roadmap for your needs
No visibility into performance bottlenecks
Why Modular outperforms
Deploy in Modular’s Cloud
We’ll handle the autoscaling of your traffic across hardware. Our AI infrastructure runs across NVIDIA and AMD without code changes, so future flexibility is also baked in.
Supported hardware:
Full production support for the following NVIDIA GPUs
H100
A100
L40S
L4
Full production support for the following AMD GPUs
MI355X
MI300X
MI250X
MI210
Achieve 30-60% lower costs with Modular on AMD hardware - Read More
Coming soon:
Custom accelerators - let us know what you want!
Hardware Independence = Business Resilience
Why Portability Matters to Your Business:
Negotiation Power
Not locked to single GPU vendor. AMD offers 30-60% cost savings. Better supply availability.
Risk Mitigation
No single point of failure. Multi-cloud without complexity. Platform vendor independence.
Deployment Flexibility:
Our Cloud or Yours
Deploy on our cloud or in your own environment, with the same capabilities and performance.
See Deployment Options
Why teams are switching to Modular
“~70% faster compared to vanilla vLLM”
"Our collaboration with Modular is a glimpse into the future of accessible AI infrastructure. Our API now returns the first 2 seconds of synthesized audio on average ~70% faster compared to vanilla vLLM based implementation, at just 200ms for 2 second chunks. This allowed us to serve more QPS with lower latency and eventually offer the API at a ~60% lower price than would have been possible without using Modular’s stack."
Latest customer case studies:
Go Deeper
Hop on a quick call to go over technical specs about your workload and see if we’re a fit.
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Thank you,
Modular Sales Team
Thank you for your submission.
Your report has been received and is being reviewed by the Sales team. A member from our team will reach out to you shortly.
Thank you,
Modular Sales Team
Get started with Modular
Schedule a demo of Modular and explore a custom end-to-end deployment built around your models, hardware, and performance goals.
Distributed, large-scale online inference endpoints.
Highest-performance endpoints to maximize ROI
Deployment in your cloud or on-premise environment.
View all features

Book a demo
Talk with our sales lead Jay!
"after wrestling with CUDA drivers for years, it felt surprisingly… smooth. No, really: for once I wasn’t battling obscure libstdc++ errors at midnight or re-compiling kernels to coax out speed. Instead, I got a peek at writing almost-Pythonic code that compiles down to something that actually flies on the GPU."
“A few weeks ago, I started learning Mojo 🔥 and MAX. Mojo has the potential to take over AI development. It's Python++. Simple to learn, and extremely fast.”
"This is about unlocking freedom for devs like me, no more vendor traps or rewrites, just pure iteration power. As someone working on challenging ML problems, this is a big thing."
“I tried MAX builds last night, impressive indeed. I couldn't believe what I was seeing... performance is insane.”
“What @modular is doing with Mojo and the MaxPlatform is a completely different ballgame.”
“Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation”
"C is known for being as fast as assembly, but when we implemented the same logic on Mojo and used some of the out-of-the-box features, it showed a huge increase in performance... It was amazing."
“I'm excited, you're excited, everyone is excited to see what's new in Mojo and MAX and the amazing achievements of the team at Modular.”
“Max installation on Mac M2 and running llama3 in (q6_k and q4_k) was a breeze! Thank you Modular team!”
“Tired of the two language problem. I have one foot in the ML world and one foot in the geospatial world, and both struggle with the 'two-language' problem. Having Mojo - as one language all the way through is be awesome.”
"It worked like a charm, with impressive speed. Now my version is about twice as fast as Julia's (7 ms vs. 12 ms for a 10 million vector; 7 ms on the playground. I guess on my computer, it might be even faster). Amazing."
Mojo destroys Python in speed. 12x faster without even trying. The future is bright!
“It’s fast which is awesome. And it’s easy. It’s not CUDA programming...easy to optimize.”
“The Community is incredible and so supportive. It’s awesome to be part of.”
“The more I benchmark, the more impressed I am with the MAX Engine.”
“Mojo destroys Python in speed. 12x faster without even trying. The future is bright!”
“Mojo can replace the C programs too. It works across the stack. It’s not glue code. It’s the whole ecosystem.”
“I'm very excited to see this coming together and what it represents, not just for MAX, but my hope for what it could also mean for the broader ecosystem that mojo could interact with.”
"Mojo is Python++. It will be, when complete, a strict superset of the Python language. But it also has additional functionality so we can write high performance code that takes advantage of modern accelerators."
"Mojo gives me the feeling of superpowers. I did not expect it to outperform a well-known solution like llama.cpp."























