Limite 1B Violetto is a math model that ships as one 2.1 GB file, and Paradigma's table puts it at 94.01% on AIME 2026, a US exam for top high school students. We put it on one RTX 4090 on our cloud and gave it the 30 problems once each. The result: 27 correct, 2 wrong, 1 with no final answer. The per-problem breakdown is in our full test of Limite 1B on AIME 2026. This post covers the serving choices, which differ from a chatbot's.
Credit first. Prignano, Cirillo, Morosini, Cerovaz, Bartolocci, Rodolà, Starace and Pappone at Paradigma trained the model from scratch. They released it under Apache 2.0 with their evaluation code, and wrote the vLLM plugin that serves it. MathArena maintains the AIME 2026 problem set on Hugging Face. We heard about Limite in the AI Search news video of 27 September. What we added: a deployable package around the plugin, runs on two different cards, and a grade for every answer.
Stock vLLM will not load it, and the error points the wrong way
vLLM is the open-source program that serves models behind an HTTP API. Limite has its own architecture, and vLLM does not know it out of the box. The code that teaches it lives in Paradigma's plugin, which vLLM only loads when an environment variable names it:
VLLM_PLUGINS=limite vllm serve paradigma-inc/limite-1b-violetto
Leave the variable out and you get "Transformers does not recognize this architecture". It sends you off to upgrade Transformers, which will not help. The plugin can only copy the whole model onto each GPU (data parallelism), never split one copy across cards. And Paradigma's verified stack pins Python 3.12, vLLM 0.26.0 and a PyTorch build for CUDA 13.0, which means an NVIDIA driver of 580 or later on the host.
Our packaged vLLM option installs plain vLLM only, so we wrote a custom backend: a small Python file that installs the versions Paradigma tested, launches vLLM with the plugin and relays requests.
The memory cap is about concurrency, not speed
The flag to understand is --gpu-memory-utilization, the share of the card's memory vLLM is allowed to claim. We ran the 4090 at 0.90, then at roughly 6 GB and 4 GB. Speed stayed flat: 211 tokens per second at 0.90, 213 and 209 at the 4 GB cap. Problems 1 and 5 came back correct each time.
A token is a chunk of text, roughly a short word. vLLM keeps a scratch record of every token in each open answer, and the cap sizes that area. At 0.90 it held 1,465,844 tokens, about eleven full-length answers. At the 4 GB cap it had room for 141,408, which is about one. Real usage ran about 1 GB above the cap, so plan for it.
At 0.12, about 3 GB, vLLM would not start at all. It needs room for one answer at the full 131,072 tokens, and proposed a limit near 40,000 instead. We left that untried: several answers ran longer.
One caveat: a capped 4090 imitates a small card's memory, not its slower chip. Only two short problems ran under the caps, and no real 8 GB or 12 GB card was tried. A real 16 GB RTX 2000 Ada answered correctly at 85 tokens per second.
Measure answer length before you size anything
Limite reasons out loud. It writes out attempts, checks them and discards the failures before answering, and you wait for all of that text. Problem 1 needed 3,203 tokens. Problem 10 needed about 101,000. The median sat near 21,000.
Log that spread first: it drives the memory cap and the batching. Batching means sending many problems at once so the GPU works on them together. With all 30 in flight, throughput peaked around 17 times one answer's speed. But short answers finished in under a minute while the batch ran 16 minutes, waiting on problem 10. Sorting problems by expected length should shorten that wait. We have not measured it.
On a 4090 at about 0.80 euros an hour, a full batch comes out under one euro cent per correct answer. One at a time, most of the card sits unused.
A fixed prompt, and an SDK gap of ours
The chat template, the format that wraps each request before it reaches the model, only accepts Paradigma's standard opening instruction. A custom system prompt or a tool definition gets rejected. Our backend strips any non-standard one, so a generic chat client still works. Non-math questions come back reframed as math, so this is no fit for chat or tool-calling agents.
One gap is ours: the current SDK release has no working chat path here, and the client hung three minutes until its timeout instead of erroring. The newer code in the SDK repository fixes it, and the issue stays open until the next release.
What the test does and does not show
Paradigma averages 32 attempts per problem. One attempt is noisier, so 27 of 30 places the model in their range, not at their exact figure. The AIME 2026 problems were public months before training, and we did not test for contamination. On ArXivMath May 2026, a newer set, Paradigma reports 25%. Our grading looked at the final boxed answer only, never at the steps that led to it.
The serving setup, the full memory ladder and each problem's outcome are in the research post, with the SDK example that deploys and grades it.
To size a card for Limite yourself, start from the memory ladder and token counts in the Limite 1B AIME 2026 write-up on inferencekey.com.
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