Frequently Asked Questions

TAHO is the execution-fabric between orchestration and silicon. It decomposes AI and HPC workloads into thread-level tasks that fit the processors you already own, reaching capacity that today sits locked away and getting far more work done per dollar.

TAHO is software. It works with the hardware you already own so your processors do far more work.

TAHO's execution-fabric optimally fits workloads to your processors. Orchestration tools schedule jobs and manage clusters. The execution-fabric decomposes workloads into tiny jobs (thread-level tasks) so each one fits on your untapped processor capacity. Because it works below orchestration and just above the silicon, it makes everything you already run faster and cheaper without requiring any changes.

For two decades, the industry's answer to "make it faster" has been a bigger boat: a faster chip, more memory, a faster network. Every one of those moves makes the pipe faster. None of them touches the actual problem. The workload itself often doesn't fit the hardware well, and that locks away most of what your hardware can do, no matter how fast the pipe is. TAHO takes a different approach. It decomposes the work into pieces that fit, so the hardware you already own finally does everything you pay for.

More hardware raises your ceiling. It doesn't make the work fit. It also typically represents a major capital expense. Most fleets leave a large share of their capacity unreachable because work doesn't fit each processor. TAHO closes that gap on the hardware you already have, so you get more out of every machine before you spend on the next one.

Usually because the work doesn't fit the hardware. AI and HPC workloads are large and complex, and no single processor runs all of them well. Forced through anyway, the work moves slowly and can't reach much of the processor's capability, so utilization reads low even while the machine is busy. Faster chips raise the ceiling without changing the fit. TAHO decomposes workloads into thread-level tasks that fit each processor, so the same machines complete far more work.

Make more of your existing hardware reachable. Most inference fleets pay for hardware their workloads can only partly use, because the work doesn't fit the processors well, so costs stay high no matter which GPUs you buy. Spot instances, right-sizing, and autoscaling change how you buy compute, not how much of it your work can reach. TAHO works underneath your serving stack, decomposing inference into thread-level tasks that fit your processors, cutting cost on the hardware you already own. It runs alongside vLLM, SGLang, and TensorRT-LLM.

TAHO isn't an orchestrator, and it isn't competing with the one you're running. Kubernetes and SLURM sit above us in the stack. TAHO works underneath, closer to the silicon, so anything running on top of us gets faster and cheaper. Your scheduler, your clusters, your pods stay exactly as they are.

No. Those all sit above us in the stack and TAHO works underneath all of them, not in place of any of them. We're not managing which jobs run where, caching results, or abstracting hardware differences. TAHO is the execution layer that makes whatever's above us run faster on the silicon you already have.

TAHO sits directly on top of the silicon, beneath orchestration, serving frameworks, inference caching, hardware-abstraction layers, and your data and storage fabric.

Yes. NVIDIA, AMD, Intel, Qualcomm, hyperscaler ASICs, across CPU, GPU, tensor, and neural processor types. TAHO works with CUDA, ROCm, oneAPI, and the rest.

Yes. TAHO works alongside serving frameworks like vLLM, SGLang, and TensorRT-LLM. Whatever you're running today stays exactly as it is.

TAHO is best for teams running high-throughput, always-on compute: AI training and inference, multi-threaded workloads, LLMs, and simulation pipelines. If compute is a large and growing line item, TAHO is built for you.

TAHO isn't built for simple web apps, basic APIs, or light, intermittent compute. If you're not pushing serious compute, you don't need TAHO.

No. TAHO runs alongside what you already have, without disrupting it. Start with one workload, prove the value, then expand at your own pace. No rip-and-replace, no major migration.

Up to 10× faster execution, up to 90% lower compute cost, on the hardware you already have.

By eliminating waste. TAHO decomposes work to the thread level so every piece fits, keeping your processors full instead of leaving you paying for capacity the work can't reach. It also avoids recomputing work your fleet has already done. The result is far more useful output from the same machines.

Yes. TAHO runs within your existing environment and adopts your current security posture, with sandboxed execution and runtime isolation. All node-to-node communication uses encrypted peer-to-peer networking, and data in transit is encrypted by default across edge, cloud, and hybrid deployments.

Yes. TAHO uses hot-reload to update your workloads in milliseconds without stopping execution. No restarts, no service interruptions.

TAHO automatically detects failures and redistributes workloads across healthy hardware and cloud platforms, without manual intervention.

No. TAHO operates below your application and orchestration layers, so your code, models, and pipelines run as they are. You adopt TAHO under what you already run rather than porting anything onto it.

All of them. TAHO supports models over 100GB and distributes them efficiently across multiple nodes.

Both. TAHO runs across cloud, on-prem, edge, and hybrid deployments, inside your own environment.

TAHO Labs was founded by Todd Smith (CEO), Michal Ashby (CTO), and Justin Gelinas (CBO). Todd scaled infrastructure at Facebook and Snap, then served as VP of Operations at Docker. Michal held architecture and ML infrastructure roles at Meta, Snap, Google, and Disney. Justin co-founded Britelite Immersive, delivering HPC systems for Fortune 500 brands, and led it through acquisition.