16–24%
average throughput gain
Multi-GPU, large models
Production inference, tuned
AI-guided systems optimization
Make every token
cost less to compute.
TuneShift adapts inference engines and kernels to your exact workload—unlocking more throughput, lower latency, and better unit economics.
Model × Hardware × TrafficNo migration. No lock-in.
The opportunity
General-purpose inference engines leave performance on the table.
Production AI is a moving target. Models, traffic, hardware, and service-level objectives change constantly. Static presets cannot account for every combination.
TuneShift continuously discovers the configuration and code your workload actually needs—without asking your team to maintain a permanent optimization function.
Measured results
10–40%
Improvement across key performance metrics in typical agentic workloads to date, with higher gains for batch workloads.
Baseline: vLLM / SGLang production stacks
19–45%
throughput gain
RAG and batched workloads
11–14%
faster kernels
vs. cuBLAS, CUTLASS & cuDNN
35%+
faster search loops
vs. leading agentic optimizers
Latency · TTFT · End-to-endThroughput · Tokens/sCost · Per 1M tokensPower · Tokens/watt/GPU
The optimization loop
Optimization, built around your reality.
01
Observe the real workload
We capture the model, hardware, architecture, traffic shape, and service-level constraints that define your production environment.
02
Generate and verify
Our AI harness creates candidate engine configurations and target code, then verifies correctness empirically and formally.
03
Search beyond intuition
Smart search explores billions of reasonable choices to uncover optima that manual tuning rarely reaches.
04
Deploy the advantage
You receive an optimized engine implementation for your target environment—without changing your stack or accepting lock-in.
Live workload tracesOptimized target engine
Two layers. One objective.
Move the entire system forward.
Inference engine customization
Custom scheduling, traffic modeling, speculative decoding depth, cache paging, eviction strategy, and shape-specialized execution.
Kernel-level optimization
Custom kernel fusion and graph capture reduce overhead while maximizing utilization under real workload variance.
SLA-aware objectives
Optimize for what matters: throughput, latency, cost per token, power efficiency—or the right balance across all four.
Built by specialists
Deep systems experience, applied by AI.
Co-founder
Vadim Telyatnikov
Serial software founder with 20 years of experience and multiple exits across revenue optimization and cost reduction. Co-founded AlphaPoint and most recently led its AI Lab.
Co-founder
Aca Gacic
PhD in Computer Engineering from Carnegie Mellon. An early pioneer in AI-driven high-performance code generation with 18+ years across ML and AI.
Michael Looney
Multi-exited founder with deep expertise in go-to-market, sales, and revenue generation in the software space. 15+ years experience scaling startups.
Pilot program
Let your own workload make the case.
We tune against your live production environment and show the results first. No migration, no lock-in, no obligation.