The Kinetic Compute Fabric
More AI.
Same Infrastructure.
More AI.
Same Infrastructure.
More AI.
Same Infrastructure.
The Kinetic Compute Fabric
More AI.
Same Infrastructure.
The Value Triads
Proven performance growth
Performance out of your existing AI infrastructure
Token yield improvement
Reduction in energy consumption
Trusted by partners
Maximize every GPU you own
Tinkerblox delivers higher performance, greater utilization, and increased token output, all without requiring any new hardware.
The Kinetic Compute Fabric
Your compute is underutilized.
Tinkerblox fixes that on the software level.
–
CPU load reduction
GPU utilization improvement
The Kinetic Compute Fabric
Your compute is underutilized.
Tinkerblox fixes that on the software level.
–
CPU load reduction
GPU utilization improvement
–
CPU load reduction
GPU utilization improvement
The Kinetic Compute Fabric
Your compute is underutilised.
Tinkerblox fixes that on the software level.
–
CPU load reduction
GPU utilization improvement
The Kinetic Compute Fabric
Your compute is underutilised.
Tinkerblox fixes that on the software level.
–
CPU load reduction
GPU utilization improvement
The Kinetic Compute Fabric
Your compute is underutilised.
Tinkerblox fixes that on the software level.
The Value Triads
Proven performance growth
Performance out of your existing AI infrastructure
Token yield improvement
Reduction in energy consumption
Trusted by partners
The Value Triads
Proven performance growth
Performance out of your existing AI infrastructure
Token yield improvement
Reduction in energy consumption
Trusted by partners
The Value Triads
Proven performance growth
Performance out of your existing AI infrastructure
Token yield improvement
Reduction in energy consumption
Trusted
by partners
The Value Triads
Proven performance growth
Performance out of your existing AI infrastructure
Token yield improvement
Reduction in energy consumption
Trusted
by partners
The Value Triads
Proven performance growth
Performance out of your existing AI infrastructure
Token yield improvement
Reduction in energy consumption
Trusted
by partners
How It Works
Tinkerblox is the execution layer below your workloads and above your compute, the point where scheduling ends and running begins
AI efficiency for workloads has lived in two places: Orchestration tools that schedule workloads, and model tools that compress them. Tinkerblox is neither. We own the high-impact execution space in between.
Runs in user space. Changes nothing beneath it
Tinkerblox installs above the OS, with no kernel changes, no driver modifications, and no rebuild of your stack. Plug and play, up and running.
Lives on the CPU. Influences the GPU
Every AI workload executes through the CPU, covering scheduling, startup, memory, and the handoff to the GPU. Tinkerblox makes that layer efficient, and the gains carry across the handoff, resulting in higher GPU utilization and more tokens out.
Never touches your models or your data
Your models run unmodified. Your data stays yours, since Tinkerblox never reads it, moves it, or changes it.
A multiplier, not a replacement
Model compression, better silicon, smarter routing: keep all of it. Tinkerblox works underneath them, and the gains stack. Optimized models on an optimized execution layer beat either alone.
How It Works
Tinkerblox is the execution layer below your workloads and above your compute, the point where scheduling ends and running begins
Software efficiency for AI has lived in two places: orchestration tools that schedule workloads, and model tools that compress them. Tinkerblox is neither. We own the high-impact space in between.
How It Works
Tinkerblox is the execution layer below your workloads and above your compute, the point where scheduling ends and running begins
Software efficiency for AI has lived in two places: orchestration tools that schedule workloads, and model tools that compress them. Tinkerblox is neither. We own the high-impact space in between.
Runs in user space. Changes nothing beneath it
Tinkerblox installs above the OS, with no kernel changes, no driver modifications, and no rebuild of your stack. Plug and play, up and running.
Lives on the CPU. Influences the GPU
Every AI workload executes through the CPU, covering scheduling, startup, memory, and the handoff to the GPU. Tinkerblox makes that layer efficient, and the gains carry across the handoff, resulting in higher GPU utilization and more tokens out.
Never touches your models or your data
Your models run unmodified. Your data stays yours, since Tinkerblox never reads it, moves it, or changes it.
A multiplier, not a replacement
Model compression, better silicon, smarter routing: keep all of it. Tinkerblox works underneath them, and the gains stack. Optimized models on an optimized execution layer beat either alone.
More Revenue
Every reclaimed point
is billable
Faster cold starts
Higher workload density
Higher token throughput
Better CPU & GPU utilization
More density
More Revenue
Every reclaimed point
is billable
Faster cold starts
Higher workload density
Higher token throughput
Better CPU & GPU utilization
More density
Lower Cost
Pay for tokens,
not overhead
CapEx deferred
Average resource reclamation
Lower cost per job
Faster startup
Lower Cost
Pay for tokens,
not overhead
CapEx deferred
Average resource reclamation
Lower cost per job
Faster startup
Less Energy
Power grid is capped.
Your output isn't
-
Reduction in Megawatt per token generation
Network traffic reduced
More output per watt
Less Energy
Power grid is capped.
Your output isn't
-
Reduction in Megawatt per token generation
Network traffic reduced
More output per watt
More Revenue
Every reclaimed point
is billable
Faster cold starts
Higher workload density
Higher token throughput
Better CPU & GPU utilization
More density
More Revenue
Every reclaimed point
is billable
Faster cold starts
Higher workload density
Higher token throughput
Better CPU & GPU utilization
More density
More Revenue
Every reclaimed point
is billable
Faster cold starts
Higher workload density
Higher token throughput
Better CPU & GPU utilization
More density
Lower Cost
Pay for tokens,
not overhead
CapEx deferred
Average resource reclamation
Lower cost per job
Faster startup
Lower Cost
Pay for tokens,
not overhead
CapEx deferred
Average resource reclamation
Lower cost per job
Faster startup
Lower Cost
Pay for tokens,
not overhead
CapEx deferred
Average resource reclamation
Lower cost per job
Faster startup
Less Energy
Power grid is capped.
Your output isn't
-
Reduction in Megawatt per token generation
Network traffic reduced
More output per watt
Less Energy
Power grid is capped.
Your output isn't
-
Reduction in Megawatt per token generation
Network traffic reduced
More output per watt
Less Energy
Power grid is capped.
Your output isn't
-
Reduction in Megawatt per token generation
Network traffic reduced
More output per watt
Workload Performance Benchmarks
Workload Performance Benchmarks
Workloads
Every AI workload runs through the CPU layer
Every AI Workload runs through CPU layer
No workload reaches the GPU without executing on the CPU first. That's the layer Tinkerblox owns, and it's why Tinkerblox is model-agnostic by architecture, not by effort.
Workload Performance Benchmarks
AI Scenarios
Business Impact
–
Lower infrastructure hours
Performance Impact
–
Faster pod startup
–
Higher throughput per node
Measured in pilot environments on Arm and Intel CPUs with NVIDIA H100 GPUs, across live inference and compute workloads.
Workload Performance Benchmarks
AI Scenarios
Business Impact
–
Lower infrastructure hours
Performance Impact
–
Faster pod startup
–
Higher throughput per node
Measured in pilot environments on Arm and Intel CPUs with NVIDIA H100 GPUs, across live inference and compute workloads.
High Concurrency LLM Inference
Zero-Latency Multi-Modal Orchestration
Autonomous Agent Economics
Serverless Agility with Dedicated Performance
Enterprise RAG Acceleration
Sustainable Infrastructure and Green ROI
Business impact
–
Lower infrastructure hours
Performance impact
–
Faster pod startup
–
Higher throughput per node
Measured in pilot environments on Arm and Intel CPUs with NVIDIA H100 GPUs, across live inference and compute workloads.
High Concurrency LLM Inference
Zero-Latency Multi-Modal Orchestration
Autonomous Agent Economics
Serverless Agility with Dedicated Performance
Enterprise RAG Acceleration
Sustainable Infrastructure and Green ROI
Business impact
–
Lower infrastructure hours
Performance impact
–
Faster pod startup
–
Higher throughput per node
Measured in pilot environments on Arm and Intel CPUs with NVIDIA H100 GPUs, across live inference and compute workloads.
High Concurrency LLM Inference
Zero-Latency Multi-Modal Orchestration
Autonomous Agent Economics
Serverless Agility with Dedicated Performance
Enterprise RAG Acceleration
Sustainable Infrastructure and Green ROI
Business impact
–
Lower infrastructure hours
Performance impact
–
Faster pod startup
–
Higher throughput per node
Measured in pilot environments on Arm and Intel CPUs with NVIDIA H100 GPUs, across live inference and compute workloads.
Step 01
Install
Package your workloads as MicroPacs using our packager. Download the Tinkerblox installer — an image, like any you've deployed and install it on your CPUs/VMs. Install time is what a VM spin-up costs you. Minutes, not months.
Step 02
Integrate
Connect Tinkerblox to your existing orchestration stack. Whether you use Kubernetes or another manager, Tinkerblox rolls out node by node using your standard orchestration pipeline. No changes to your AI frameworks or model deployment workflows whatsoever.
Step 03
Execute
Start your AI workloads as you normally would. As your CPU begins scheduling, startup and memory management, Tinkerblox automatically detects the streamlining opportunities optimizing every activity in real time.
Step 04
Measure
View the gains directly in your existing observability stack. Tinkerblox hooks seamlessly into Prometheus and Grafana existing observability stack surfacing granular execution metrics — like TTFT & CPU-GPU utilization — that were previously invisible to your team
Step 05
Experience
Witness the multiplier effect. With more tokens per second, higher workload density & lower energy consumption, your infrastructure is now running at its physical limit — without a single manual tuning step.
Step 01
Install
Package your workloads as MicroPacs using our packager. Download the Tinkerblox installer — an image, like any you've deployed and install it on your CPUs/VMs. Install time is what a VM spin-up costs you. Minutes, not months.
Step 02
Integrate
Connect Tinkerblox to your existing orchestration stack. Whether you use kubernetes or another manager, Tinkerblox rolls out node by node using your standard orchestration pipeline. No changes to your AI frameworks or model deployment workflows whatsoever.
Step 03
Execute
Start your AI workloads as you normally would. As your CPU begins scheduling, startup and memory management, Tinkerblox automatically detects the streamlining opportunities optimizing every activity in real time.
Step 04
Measure
View the gains directly in your existing observability stack. Tinkerblox hooks seamlessly into Prometheus and Grafana surfacing granular execution metrics — like TTFT & CPU-GPU utilization — that were previously invisible to your team.
Step 05
Experience
Witness the multiplier effect. With more tokens per second, higher workload density & lower energy consumption, your infrastructure is now running at its physical limit — without a single manual tuning step.
How You Start
Prove it on your own nodes
Our engagement model delivers two artifacts i.e. the MicroPac packager and the Tinkerblox installer. Your team validates the impact within your own environment.
Zero data access
Tinkerblox writes its logs directly to your existing observability stack. Nothing leaves your environment i.e. no data, no models, no workloads, no logs.
Onboarding, minus the friction
Four questions is all it takes: hardware architecture (x86 or Arm), operating system (Linux), orchestration layer (Kubernetes or Slurm), and deployment scale. No scoping calls. No integration projects. No waiting.
Deploy in minutes, not months
No hardware to rack. No stack to rebuild. In an orchestrated environment, Tinkerblox rolls out node by node like any image.
Zero data access
TinkerBlox writes its logs directly to your existing observability stack. Nothing leaves your environment i.e. no data, no models, no workloads, no logs.
Onboarding, minus the friction
Four questions is all it takes: hardware architecture (x86 or Arm), operating system (Linux), orchestration layer (Kubernetes or Slurm), and deployment scale. No scoping calls. No integration projects. No waiting.
Deploy in minutes, not months
No hardware to rack. No stack to rebuild. In an orchestrated environment, Tinkerblox rolls out node by node like any image.
How You Start
Prove it on your own nodes
You don't send us anything. We send you two artifacts — the MicroPac packager and the Strym installer — you measure the impact yourself.
You don't send us anything. We send you two artifacts — the MicroPac packager and the Tinkerblox installer — you measure the impact yourself.
You don't send us anything. We send you two artifacts — the MicroPac packager and the Tinkerblox installer — you measure the impact yourself.
Zero data access
Strym writes its logs into your Prometheus/Grafana stack. Nothing flows to us — not your data, not your models, not your workloads, not your logs.
Tinkerblox writes its logs into your Prometheus/Grafana stack. Nothing flows to us not your data, not your models, not your workloads, not your logs.
Strym writes its logs into your Prometheus/Grafana stack. Nothing flows to us not your data, not your models, not your workloads, not your logs.
Onboarding, minus the friction
Onboarding, minus the friction
We ask four sanity questions — hardware architecture (x86 or Arm), OS (Linux), orchestration (Kubernetes or Slurm), scale — and send you the two artifacts. That's the whole onboarding.
We ask four sanity questions — hardware architecture (x86 or Arm), OS (Linux), orchestration (Kubernetes or Slurm), scale and send you the two artifacts. That's the whole onboarding.
We ask four sanity questions — hardware architecture (x86 or Arm), OS (Linux), orchestration (Kubernetes or Slurm), scale and send you the two artifacts. That's the whole onboarding.
Deploy in minutes, not months
No hardware to rack. No stack to rebuild. In an orchestrated environment, Strym rolls out node by node like any image.
No hardware to rack. No stack to rebuild. In an orchestrated environment, Tinkerblox rolls out node by node like any image.
No hardware to rack. No stack to rebuild. In an orchestrated environment, Tinkerblox rolls out node by node like any image.
Focus
Value Delivered
Where Tinkerblox delivers immediate value at scale.
Where Tinkerblox delivers immediate value at scale.
Neo-Clouds
Every cold start
is margin
Transforming under-utilized clusters into Profit
GPUs bill raises for every second wait on the CPU cold starts, agentic gaps, pre-processing stalls. Tinkerblox eliminates that overhead at the execution layer, so the cluster you already own converts more of every GPU-hour into billable tokens. No added capacity, no rewrites, no rip-and-replace.
Data Centers
More revenue
per megawatt
Maximize compute density
per Megawatt
Power is your ceiling. Every watt burned on CPU execution overhead produces no billable tokens. Tinkerblox turns that overhead into output more tokens per watt for your tenants, more revenue per MW for you, less heat in the same cooling envelope. No new racks. No rip-and-replace.
Inference Operators
Same GPU-hours. More tokens. Fewer SLA breaches
Same GPU-hours. More tokens. Fewer SLA breaches
Cost per token SLA has two levers: GPU-hour consumed, and token output. Tinkerblox removes every millisecond of CPU execution overhead in between i.e. pre-processing delays for first token, a cold start on every model switch so your cost basis stays put and what it yields goes up.
Inference Operators
Same GPU-hours. More tokens. Fewer SLA breaches
Cost per token SLA has two levers: GPU-hour consumed, and token output. Tinkerblox removes every millisecond of CPU execution overhead in between i.e. pre-processing delays for first token, a cold start on every model switch so your cost basis stays put and what it yields goes up.
Why Tinkerblox
No other software delivers execution efficiency at the CPU to GPU boundary without touching your stack
Tinkerblox runs on the CPU, the layer that schedules, feeds, and hands off to every GPU you own. That handoff is where execution efficiency is won or lost. Orchestrators schedule around it. Model tools shrink what crosses it. Only Tinkerblox makes the handoff itself efficient.
01
Zero stack changes
No kernel changes. No orchestration reconfiguration. No model or framework changes. Your stack on Monday is your stack on Friday, just faster and more efficient.
02
Hardware agnostic
If it runs on Linux, Tinkerblox runs on it. x86 or ARM architectures across any CPU vendor. CPU-only clusters or heterogeneous CPU-GPU nodes. Managing workloads across cloud, on-prem or Co-Lo. Tinkerblox adapts seamlessly
03
The handoff is ours
Tinkerblox is the only software explicitly engineered to act at the exact intersection where the CPU meets the GPU, the point where inefficiency actually accumulates.
04
Observability, in your stack
Tinkerblox feeds granular, high-fidelity execution and pipeline metrics directly into the open standards you already trust, including Prometheus and Grafana. Gain a level of visibility into the compute fabric that was previously impossible to capture.
05
Rollback anytime
Trying Tinkerblox is a decision you can reverse in minutes, but given the immediate returns, we promise you won't.
Why Tinkerblox
Why Tinkerblox
No other software delivers execution efficiency at the CPU to GPU boundary without touching your stack
Tinkerblox runs on the CPU, the layer that schedules, feeds, and hands off to every GPU you own. That handoff is where execution efficiency is won or lost. Orchestrators schedule around it. Model tools shrink what crosses it. Only Tinkerblox makes the handoff itself efficient.
01
Zero stack changes
No kernel changes. No orchestration reconfiguration. No model or framework changes. Your stack on Monday is your stack on Friday — just faster and more efficient.
No kernel changes. No orchestration reconfiguration. No model or framework changes. Your stack on Monday is your stack on Friday, just faster and more efficient.
02
Hardware agnostic
If it runs on Linux, Strym runs on it. x86 or ARM architectures across any CPU vendor. CPU-only clusters or heterogenous CPU-GPU nodes. Managing workloads across cloud, on-prem or Co-Lo. Tinkerblox adapts seamlessly
If it runs on Linux, Strym runs on it. x86 or ARM architectures across any CPU vendor. CPU-only clusters or heterogenous CPU-GPU nodes. Managing workloads across cloud, on-prem or Co-Lo. Tinkerblox adapts seamlessly
03
The handoff is ours
Tinkerblox is the only software explicitly engineered to act at the exact intersection where the CPU meets the GPU — where inefficiency actually accumulates.
Tinkerblox is the only software explicitly engineered to act at the exact intersection where the CPU meets the GPU, the point where inefficiency actually accumulates.
04
Observability, in your stack
Tinkerblox feeds granular, high-fidelity execution and pipeline metrics directly into the open standards you already trust, including Prometheus and Grafana. Gain a level of visibility into the compute fabric that was previously impossible to capture.
Tinkerblox feeds granular, high-fidelity execution and pipeline metrics directly into the open standards you already trust, including Prometheus and Grafana. Gain a level of visibility into the compute fabric that was previously impossible to capture.
05
Rollback anytime
Trying Tinkerblox is a decision you can reverse in minutes — but given the immediate returns, we promise you won't.
Trying Tinkerblox is a decision you can reverse in minutes, but given the immediate returns, we promise you won't.
© 2026 Tinkerblox Inc.
© 2026 Tinkerblox Inc.
© 2026 Tinkerblox Inc.
© 2026 Tinkerblox Inc.
