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Luminal vs Infron
Luminal compiles models you bring into faster GPU code. Infron routes calls to 400+ hosted models from many providers.
By The Subconscious Team · Updated
Luminal vs Infron: key differences
Luminal's open-source compiler turns a model into native GPU kernels ahead of time and sells early-access serverless endpoints and an on-prem license, reporting 36K tokens per second on GPT-OSS 120B across 8 H100s. Infron is a gateway: one OpenAI-compatible API in front of 400+ models from 100+ providers, at provider rates plus a 3% to 5% fee on credit top-ups, with fallbacks, region pinning and a 99.9% uptime SLA on dedicated throughput.
These sit at opposite ends of the stack. Luminal is for teams serving their own model who want more throughput per GPU. Infron is for teams calling other people's models who want one key and failover.
What Luminal and Infron do
Luminal
Luminal builds an inference compiler. Where vLLM and SGLang interpret a model at runtime, Luminal compiles it ahead of time into native kernels for GPUs and ASICs. Models get lowered to a small graph of 15 primitive ops, and the compiler searches over fusion, tiling, memory and scheduling choices instead of relying on hand-written rules, which it says can find optimizations like Flash Attention on its own. The compiler is open source in Rust under Apache 2.0 or MIT, runs on CUDA and Metal with ROCm on the roadmap, and works as a torch.compile backend.
Example models: GPT-OSS 120B, Llama 3 8B
Full Luminal profile
Infron
Infron is a US-based AI gateway and inference platform. One OpenAI-compatible API reaches 400+ models from 100+ providers, including DeepSeek, Qwen, Claude, Gemini and GPT through what Infron calls official partner routes, plus media and search models. Teams set provider preferences and fallbacks, see usage and billing in one place, and can bring their own provider keys at no fee. Lawrence Xu is CEO and co-founder Andrew Zheng is CTO.
Example models: DeepSeek, Qwen, Claude, Gemini, GPT
Full Infron profileShould you choose Luminal or Infron?
Luminal vs Infron at a glance
| Attribute | Luminal |  Infron |
|---|---|---|
| Model access | Bring your own weights | Closed and open, 400+ models |
| Flagship models | No public catalog | DeepSeek, Qwen, Claude, Gemini, GPT |
| Speed | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) | Unknown |
| Price | Pay per use; rates not published | Provider rates; 3–5% top-up fee |
| Customization | Compiles any PyTorch or HF model | Custom deployments |
| Deployment | Serverless (early access), on-prem license | Gateway API, dedicated, BYOK |
| Long context | Unknown | Varies by model |
Frequently asked questions
What is the difference between Luminal and Infron?
Luminal compiles models you bring into faster GPU code. Infron routes calls to 400+ hosted models from many providers.
When should I choose Luminal over Infron?
Compiling a custom model into fast GPU code; An open-source engine to self-host; On-prem deployments with SLAs.
When should I choose Infron over Luminal?
Hosted models with no GPUs to manage; Closed and open models on one key and one bill; Automatic failover across providers.
Is Luminal or Infron cheaper?
Luminal: Pay per use; rates not published. Infron: Provider rates; 3–5% top-up fee. The cheaper choice depends on the model and workload.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.