Inference
Without
The Wait.
Compress transformer architectures. Route GPU memory tighter. Production latency drops before your team finishes standup.
Latency
p99 response time
Throughput
tokens per second
GPU Memory
VRAM utilization
LLaMA-70B · NVIDIA A100 · batch_size=64// optimization_pipeline
The Full Stack.
Every layer. Every gain.
Scroll through each optimization layer. Interact with the controls — every adjustment shows real latency impact, not projections.
Quantization
Bit-precision tuning
Reduce floating-point precision. Each bit-width halves memory footprint and cuts compute cycles — with minimal accuracy loss on modern architectures.
Latency
GPU Memory
Precision Blocks · 32-bit
Graph Fusion
Operator merging
Computation Graph
Merge adjacent operators into single CUDA kernels. Eliminates memory round-trips between ops — the biggest hidden cost in transformer inference.
Kernel Calls
Layer Latency
# Enable fusion to merge operatorsKernel Auto-Tuning
Hardware-native kernels
Auto-select CUDA kernels optimized for your exact GPU architecture. No manual tuning — Infer profiles your hardware and compiles the fastest path.
TF32 matmulFlash-Attention v2CUDA Graph captureThroughput
p99 Latency
Compute
Memory BW
vs. ~45% avg with unoptimized frameworks
Dynamic Batching
Adaptive throughput
Coalesce variable-length requests into optimal batches in real time. Continuous batching inserts new sequences mid-flight — eliminating idle GPU cycles between requests.
Continuous Batching
Insert sequences mid-flight
Request Queue · Live
2 active · 6 queued
Throughput
t/s
Latency
ms
GPU Util
util
// benchmark_results
Numbers Don't Lie.
Reproducible benchmarks. Same models, same hardware, same datasets — before and after Infer optimization.
Annual cloud savings
Median across 47 enterprise deployments in 2025
Models optimized
LLMs, diffusion, vision, audio — all architectures
Avg GPU cost reduction
Without accuracy loss on INT8 quantized models
Model Benchmark Matrix
| Model | Hardware | Baseline | Optimized | Speedup | Memory | Use Case |
|---|---|---|---|---|---|---|
| LLaMA-70B | A100 80GB | 142ms | 11ms | 12.9x | 24→6.1 GB | Chat / RAG |
| Mistral-7B | A10G | 67ms | 8ms | 8.4x | 14→3.2 GB | Edge inference |
| Stable Diffusion XL | A100 | 4800ms | 380ms | 12.6x | 18→4.8 GB | Image gen |
| Whisper Large v3 | L4 | 890ms | 95ms | 9.4x | 10→2.4 GB | Transcription |
| CLIP ViT-L/14 | A10G | 38ms | 3ms | 12.7x | 8→1.9 GB | Embeddings |
| Falcon-40B | H100 | 88ms | 5ms | 17.6x | 20→5.1 GB | Completion |
// field_reports
From Production.
"We were spending $340K/month on A100s for our recommendation engine. After Infer, same throughput at $91K. The latency drop from 134ms to 14ms was a bonus we didn't expect."
Marcus Webb
ML Platform Lead
Meridian Commerce
"Our edge deployment on A10Gs was hitting 67ms for object detection — unacceptable for real-time conveyor line inspection. Infer got us to 9ms. The line runs at full speed now."
Priya Anand
CV Engineering Lead
Vantage Robotics
"I handed our HuggingFace model IDs to the Infer sandbox on a Tuesday. By Thursday our inference cluster was reconfigured and latency was already dropping. No code changes on our side."
Dani Kowalski
CTO
Pulse AI
// cost_projection
Your cloud bill, before and after.
Paste your HuggingFace model ID. Get a projected cost breakdown in 90 seconds.
// pricing
Deploy Fast.
Start free. Scale when the numbers prove themselves — and they will.
Sandbox
forever
Run any HuggingFace model through the optimizer. See projected gains before spending a dollar.
- ✓Up to 3 model optimizations/mo
- ✓Latency projection report
- ✓INT8 quantization
- ✓Community support
Production
/mo · billed annually
Full optimization stack for teams running real-time inference in production.
- ✓Unlimited model optimizations
- ✓All 4 optimization layers
- ✓Hardware-specific kernel tuning
- ✓Dynamic batching + continuous
- ✓Priority Slack support
- ✓SLA: 99.9% uptime
Enterprise
volume pricing
Dedicated optimization engineers, custom SLAs, and private deployment for six-figure compute budgets.
- ✓Everything in Production
- ✓Dedicated ML engineer
- ✓Private cluster deployment
- ✓Custom model architectures
- ✓Executive cost reporting
- ✓Bespoke SLA