The Production Engine for
Inference Intelligence.
Deploy your custom models with built-in Foresight optimization. Run live A/B tests on inference stacks, and promote the winner to production with a single click.
Roofline-Optimized Serving
Know Your Bottleneck. Serve Faster.
Most inference stacks treat every request the same. Metis Prism uses Roofline Model Analysis to understand whether you're memory-bound (batch size matters) or compute-bound (parallelism matters).
Memory-bound? We optimize batch coalescing and KV cache. Compute-bound? We tune tensor parallelism and precision. The right optimization for the right bottleneck.
# Inference Stack A/B Testing
$ prism inference experiment create --name llama3-quant-test
> Candidate A: Llama-3-70B (FP16) @ vLLM
> Candidate B: Llama-3-70B (AWQ-INT4) @ TensorRT-LLM
[+] Traffic Split: 50/50
[+] Collecting Metrics (Latency, Perplexity)...
>>> Winner Detected: Candidate B (AWQ-INT4)
- Latency: -40%
- Quality Drop: <1% (PPL)
$ prism inference promote candidate-b --to productionLifecycle Management
The A/B Test for Your Infrastructure.
Don't guess which stack is better. Metis Prism allows you to deploy Candidate Stacks alongside your production fleet using FgAD Model Passports. The passport carries training telemetry directly into the inference configuration for optimal batch sizing and KV cache allocations.
Compare latency, throughput, and quality in real-time. Promote the winning configuration to production without downtime.
Context Optimization
RAG is an Infrastructure Problem.
The quadratic cost of attention makes large-context RAG too expensive for production. Metis Prism moves RAG optimization from the application layer down to the kernel.
- โAdversarial Context Pruning: Drops irrelevant chunks before they enter the KV Cache, saving massive compute.
- โPrivacy-Preserving Compressed RAG: Routes highly compressed chunk embeddings to specialized inference pods over secure VPCs.
- โPre-Filled KV Caches: Enterprise documents (like internal handbooks) are always kept hot in memory. Zero input-token latency.
# Context Optimization Config
deployment:
rag_profile: "enterprise_high_density"
# Keep standard docs hot in VRAM
prefill_cache:
- "s3://docs/employee_handbook.pdf"
- "s3://docs/api_reference.pdf"
compression:
enabled: true
mode: "privacy_preserving" # Only compressed embeddings forwarded
target_reduction: 0.85 # 85% fewer tokensSelf-Tuning Kernels
Kernel-Level Speed. Zero Config.
We don't just wrap inference enginesโwe optimize them. Our kernel layer continuously profiles and tunes for your specific traffic patterns.
- โFlashDecoding + PagedAttention: Fused kernels for maximum throughput.
- โSpeculative Decoding: Draft models accelerate generation by 2-3x.
- โArchitecture-Aware: Learns from your hardware to improve over time.
Foresight Intelligence
Predictive Scaling, Smart Routing, and Explainable Predictions that adapt to your traffic in real-time.
Cost-Based Routing
Route simpler queries to smaller models automatically. Same quality, 60% cheaper. Our router learns your traffic patterns.
Predictive Pre-Warming
Spin up replicas BEFORE the traffic spike. We analyze your patterns to predict demand hours ahead.
Glassbox Explainability
Every prediction comes with full transparency. See why we predicted what we predictedโconfidence intervals included.
Green AI Dashboard
Sustainability Meets Speed.
Every kernel optimization saves money AND the planet. Inference OS calculates the carbon impact of your workloads and shows you exactly how much COโ you're saving.
- โCarbon Footprint Tracking: Per-request COโ calculations based on region and hardware.
- โESG Reports: One-click sustainability reports for investors and compliance.
- โAnnualized Projections: See your yearly savings in dollars and carbon.
The Token Factory for Agent OS.
Agentic workloads demand massive throughput. Inference OS abstracts the silicon to create a dedicated, high-speed token generation layer that feeds your agentsโon any hardware.
๐๏ธ Framework Agnostic
First-class support for every inference engine. We optimize whichever one fits your workload best.
๐พ Silicon Agnostic
One deployment config, every accelerator. We map optimized kernels to any hardware.
# metis.yaml: The Token Factory
deployment:
name: "llama-3-throughput-cluster"
model: "llama-3-70b-instruct"
# Write Once, Run on Any Silicon
targets:
- accelerator: "nvidia-h100" # Uses Optimized CUDA + FlashDecoding
- accelerator: "apple-m3-max" # Uses Metal + MLX
- accelerator: "google-tpu-v5" # Uses XLA + JAX
optimization:
roofline: auto # Detect bottleneck, apply fix
engine: auto # Metis selects best runtime per target
quantization: fp8 # Tensor Core optimalKernel Marketplace
Publish your optimized inference kernels. Monetize your ML engineering expertise. Discover community-validated optimizations for your specific models.
Publish
Share your winning kernel configs for Llama, Mistral, Qwen, and more.
Monetize
Set your price. Earn 70% of every download. Turn expertise into revenue.
Discover
Find optimizations verified on real workloads with performance guarantees.
Ready to Serve Smarter?
Stop leaving throughput on the table. Inference OS learns from every request to make your next response faster, cheaper, and greener.