Memory pressure
Large contexts and cache growth increase memory movement and reduce the practical capacity of existing clusters.
Latency isn't just a technical metric—it's a revenue killer. Amazon found that every 100ms of latency costs them 1% in sales. E-commerce studies show that a 100ms delay can drop conversions by up to 7%. Beyond e-commerce, user satisfaction drops 16% for every additional second of latency. 360Inference can solve these issues by providing significant optimisations. Read on, if this resonates with you.
Context Aware Semantic Memory Management
Inference-engine optimisation for existing GPU clusters.
360Inference helps AI infrastructure teams serve more tokens on existing GPU clusters by making inferencing runtimes smarter about KV cache placement, request scheduling, context reuse and memory management.
No model retrainingNo hardware replacementRuntime-level optimisationHardware and model independent thesis
The bottleneck has shifted
AI infrastructure is constrained by memory, context and scarce GPU hours.
High-end GenAI applications are increasing demand for GPU hours faster than operators can economically supply them. At the same time, memory bandwidth, memory capacity and long-context behaviour are becoming central constraints — not just raw accelerator compute.
Runtime inefficiency
Generic runtime policies can treat useful context and low-value context too similarly, creating avoidable work.
GPU-hour scarcity
Operators need more output from existing infrastructure because additional high-end GPU capacity is expensive and constrained.
Cost per token
When memory and scheduling are inefficient, organisations pay more per token served and refresh hardware earlier than necessary.
The 360Inference approach
Make the inference engine smarter about memory.
The immediate story is inference engine optimisation. The broader thesis is hardware and model independent software efficiency that can complement modern serving stacks rather than forcing a platform replacement.
Public site, protected detail.
This public site intentionally keeps sensitive implementation detail light. Benchmarks, technical tables, roadmap detail and investor materials sit behind the investor/customer access area.
Core
Inference Engine First
Built around inference engine-specific scheduling and KV cache management, rather than a wholesale infrastructure replacement.
Deployment
Non-disruptive adoption
Designed to work with existing models, clusters and application environments without model retraining.
Efficiency
More output from current hardware
Optimisation targets the software layer between incoming inference traffic and accelerator resources.
Pilot
Measure before expansion
Define customer workloads, implement memory-aware policies and measure against baseline inference accelerator engine performance.
Benchmark-led, not claim-led
Evidence is available for qualified investor and customer review.
360Inference is being developed around benchmarked improvements in throughput, latency and runtime consistency. Public messaging avoids exposing detailed implementation tables or sensitive product IP.
Public proof summary
Early measured testing shows throughput and latency uplift under controlled benchmark conditions. Detailed figures, test environment and workload assumptions are available in the protected investor area.
Performance depends on model, workload, traffic pattern, context length, runtime configuration and hardware environment.
Pilot pathway
Pilot on a real workload before committing further.
360Inference is intended to be evaluated against customer-specific inference traffic, baseline runtime behaviour and measurable operating objectives.
1Baseline
Capture current throughput, latency and utilisation.
2Profile
Identify context, cache and scheduling patterns.
3Optimise
Apply memory-aware policies to a subset of traffic.
4Measure
Compare against the baseline and decide next steps.
Founder credibility
Built from deep systems and high-assurance architecture experience.
360Inference is led by Shantanu Bhattacharya, Founder and CEO of 360Sequrity, drawing on enterprise architecture, cybersecurity, operating-system-level thinking and complex government/enterprise delivery experience.
Lead Developer credibility
Built from expertise in AI Stack.
Urmilla Ghalley is a lead developer for 360 Inference, known for her knowledge in AI Stack and expertise in software development, infrastructure management, system administration thinking and complex software delivery.
Marketing and Sales Advisor credibility
Built from senior leadership expertise in Sales and Marketing.
Laurance Garner has held senior leadership roles in multi-nationals and startups alike, and is a sales and marketing advisor and strategist for 360 Inference. He had held very senior positions in organisations of various sizes and stages.
Startup Advisor credibility
Built from 360 degrees advisory expertise in building startups.
Atul Shinde has advised many startups in bootstrapping, fundraising, and teething issues. He is a startup advisor and strategist for 360 Inference. He had held very senior positions in organisations of various sizes and stages.
Investor and Advisor credibility
Built from senior leadership expertise in Sales and Marketing.
PhD in Computer Science, Stanford University, with >30 years of cybersecurity experience. Professor Emerita of University of Illinois.
Government Led Alliance
AI CoLab
Built from Australian Government initiative to help innovative startups in Gen AI sector.
Purpose-driven allies from governments, nonprofits, industry and academia, combining our strengths to advance inclusive, impactful uses of AI.
Testing and Exploration Partner
Shape Policy
Partner to open 360 Inference to new horizons.
Shape Policy is a Canberra-based policy and technology company with a mission to help public-interest organisations adopt and apply AI safely, responsibly, and effectively in policy and knowledge work.
Design Partner
TensorMem
Delivering AI Stack optimisation.
TensorMem is a software-defined inference memory orchestration platform focused on improving AI inference efficiency through KV cache lifecycle management across heterogeneous memory tiers and distributed infrastructure.Next step
Get more inference from the infrastructure you already run.
Request a pilot discussion or access the protected investor/customer area for benchmark and roadmap detail.