Curated, not crowded
We select models with clear application value, then engineer each path for production.
Model engineering by CantorAI
Selected AI models.
Tuned for the hardware you ship.
Garnet turns a focused set of capable open models into production packages for NVIDIA and Intel hardware—without a Python inference stack.
LLMVLMASRTTS
GARNET RUNTIMEENGINE READY
XMODEL IR
PAGED KV
NATIVE I/O
BackendTensorRT
PrecisionBF16 / INT4
ExecutionNative
Hardware-specific
Profiles are built for the backend, precision, memory, and latency target you actually deploy.
Application-ready
Validated manifests, native preprocessing, engine caching, and a stable serving interface.
Selected catalog
Four model paths.
One native runtime.
Choose an engineered package for your workload. Sign in to request access, review compatible profiles, or discuss a target we do not list yet.
LLM · TEXT GENERATION VALIDATED
Qwen3 1.7B
Compact language generation with native chat templating, paged KV cache, and optimized decode.
TensorRT BF16OpenVINO INT4CPU · GPU
31.87decode tok/s
i9-14900K · INT4
Request package →
i9-14900K · INT4
VLM · IMAGE + TEXT ENGINEERING PREVIEW
Qwen3-VL 2B
Vision-language inference with native image preprocessing, MRoPE, continuous batching graphs, and paged KV.
TensorRT BF16NVIDIA GPUJPEG input
Request access →
ASR · SPEECH TO TEXT ENGINEERING PREVIEW
Qwen3-ASR 0.6B
Native WAV ingest, resampling, log-mel transform, audio encoding, and text decoding in one compiled path.
TensorRT BF1616 kHz inputNative frontend
Join preview →
TTS · TEXT TO SPEECH ENGINEERING PREVIEW
Qwen3-TTS 0.6B
CustomVoice generation with native codec reconstruction and 24 kHz waveform output.
TensorRT BF1624 kHz outputSpeaker selection
Join preview →
Garnet Runtime
Model code is not
a deployment plan.
Garnet captures a backend-neutral XLang tensor graph, validates it, and lowers it into an executable designed for the selected hardware.
- ✓ Native C++ execution—no Python process at inference
- ✓ TensorRT and OpenVINO lowering from one model graph
- ✓ Paged KV memory, fixed decode buckets, and engine caching
- ✓ Validated model manifests and application-facing APIs
COMPILED MODEL PIPELINE
01
Selected modelSafetensors + configuration
Qwen302
XModel graphBackend-neutral XLang IR
.x03
Target profileBackend · precision · shape
JSONNVIDIATensorRTGPU engines
INTELOpenVINOCPU / GPU
Measured, not imagined
Performance with context.
Every number names the model, hardware, precision, and workload behind it.
527.9 tok/s
Qwen3-1.7B · batch 4
Masked continuous decode on RTX 4080 using official BF16 weights and GPU-resident paged KV storage.
3.33×
Garnet vs. Ollama
31.87 vs. 9.56 decode tok/s on an i9-14900K, comparing Garnet INT4 with the tested Ollama package.
Speed is only useful when the output stays useful.
Garnet validation records correctness, response quality, cold-load behavior, cache state, and unsupported hardware—not just the best token counter.
Ask for benchmark details →Internal engineering measurements. Results vary by model, prompt, driver, operating system, and engine cache state. Comparisons are not precision-equivalent unless explicitly stated.
Custom model engineering
Your model.
Your hardware.
A measured path to production.
Have a model or device target outside the catalog? CantorAI can evaluate the graph, memory budget, precision strategy, native I/O path, and application packaging.
Start an optimization review →- 01Profile
Model, workload, hardware, latency, memory, and quality targets.
- 02Engineer
Graph lowering, quantization, kernels, scheduling, and native I/O.
- 03Validate
Correctness, quality, throughput, latency, startup, and recovery.
- 04Package
A versioned model package ready for your application.
Ready to run closer to the hardware?