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CPU sandbox throughput is the amount of useful work a sandboxed environment completes per unit of time under a stated workload and configuration. For AI agents, that can mean test jobs, compilations, tool calls or other tasks completed by already-running sandboxes. It is different from provisioning throughput: how quickly an orchestrator creates environments. An agent can execute CPU instructions efficiently yet still feel slow if sandbox creation, system calls, filesystem access or orchestration queues are the bottleneck.
The right benchmark therefore follows the full lifecycle—request, provisioning, execution, result delivery and teardown—and labels exactly which stage and statistic it measures.
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CPU sandbox throughput: the precise definition
Throughput is a rate. In this context, it is completed CPU-relevant work divided by elapsed time under a declared workload, concurrency level and resource allocation. The unit might be benchmark events per second, completed jobs per minute, compilations per hour or agent tasks per second. Without the unit and test conditions, the word “throughput” is incomplete.
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Throughput is not latency
Latency is the time one operation or task takes; throughput is how much work finishes over a period. A system may have low single-request latency but poor throughput when many agents compete for CPU, or high individual latency while maintaining a high completion rate through batching and concurrency. A credible result states whether it reports an average, median, percentile tail, rate or a combination.
Execution is not provisioning
Execution tests start with an environment that is already usable. Provisioning tests include some combination of image preparation, isolation setup, resource assignment, networking, readiness checks and teardown. Intel’s CubeSandbox comparison is primarily a create/delete latency-under-concurrency test, not a measurement of CPU instructions completed per second.
Why the distinction changes an agent’s user experience
An agent request commonly follows this sequence:
- A request arrives and the controller selects or creates a sandbox.
- The image, dependencies, CPU quota and memory limit become available.
- The agent runs code, tests, tools or model-supporting workloads.
- Results and logs return to the orchestrator.
- The environment is retained for reuse or torn down.
For a long-running task in a warm environment, the execution stage can dominate. For thousands of short-lived tasks, the time to obtain a usable sandbox and the controller’s queue can dominate even when the processor executes instructions quickly. Tasks that make frequent system calls, read and write many small files or perform network operations can also spend more time crossing mediated sandbox boundaries than doing arithmetic.
What sandboxing does to CPU work
gVisor’s performance guidance states that it “does not perform emulation or otherwise interfere with the raw execution of CPU instructions by the application.” That statement applies narrowly to the instruction execution itself. Costs can appear when the workload crosses a sandbox boundary, especially for system calls and other mediated operations. See the gVisor Performance Guide.
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The guide separates structural costs—additional isolation layers and interception paths—from implementation costs in particular subsystems. Consequently, a CPU microbenchmark can look close to native execution while a filesystem-heavy or network-heavy program behaves differently. The guide notes that the cost principally affects system-call-bound applications, including high-performance data stores and static network services.
Workload patterns that produce different results
| Workload | What usually limits it | Useful output metric |
|---|---|---|
| Pure CPU calculation | Available vCPU capacity, scheduling and CPU quota | Iterations or benchmark events per second |
| Compilation and test suites | CPU plus process creation, filesystem and dependency access | Builds or test suites completed per minute |
| Many short tool calls | System-call and orchestration overhead, startup time | Completed tool tasks per second and tail latency |
| Filesystem-heavy jobs | File operations, metadata, storage path and sandbox mediation | Jobs completed and p95/p99 task latency |
| Network-heavy jobs | Network stack, policy checks, service latency and system calls | Successful tasks per second with error and tail rates |
How to read published figures without overclaiming
Every number needs its runtime, platform, hardware, limits, concurrency and measured statistic. A vendor result describes the tested configuration, not a universal ranking of CPUs or sandbox technologies.
gVisor’s documented performance baseline
The gVisor guide says its detailed measurements used uniform Google Compute Engine n1-standard-4 Broadwell virtual machines, Debian GNU/Linux 9 and kernel 4.19.0-0. Unless otherwise stated, tests used the ptrace platform. The guide identifies ptrace as having the highest structural costs and presents it to explain the performance model; it generally recommends Systrap for better performance in most current deployments. Results from that baseline should therefore be treated as mechanism-oriented evidence, not a guaranteed result for a different runtime mode, kernel, host or workload.
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A 2026 article contributed by Tencent engineers reports more than 74,000 side-by-side runsc and runc comparisons, with approximately 1.7% of test cases showing compatibility issues that the team says it investigated and resolved. It also reports millions of gVisor sandboxes running daily for Agentic-RL training. These figures establish test scale, reported compatibility experience and deployment scale; they are not a CPU-throughput penalty or a per-host rate. Read the account at gVisor’s Agentic-RL article.
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Intel’s 2026 provisioning comparison
Intel tested CubeSandbox in September 2026 by increasing aggregate concurrency from 100 to 350 and tracking average sandbox creation latency. Each sandbox had two CPUs and approximately 2 GiB of memory. Intel used 200 ms as a reference target and reported the following point at which average creation latency passed that target:
| Processor platform | Concurrent creation requests before exceeding 200 ms average | What the figure measures |
|---|---|---|
| Intel Xeon 6990E+ | Approximately 313 | Provisioning concurrency in Intel’s CubeSandbox create/delete test |
| Intel Xeon 6980P | Approximately 277 | Provisioning concurrency in the same reported test |
| AMD EPYC 9965 | Approximately 246 | Provisioning concurrency in the same reported test |
Intel says the 6990E+ platform supported roughly 27% more provisioning concurrency than the EPYC 9965 within that target. These are vendor-reported results for a specific setup. They do not show how many application instructions each CPU executes per second, and they should not be used to predict an agent’s execution speed without matching the runtime, image, storage, controller and workload. Intel explains the test and its limitations at its sandboxed-agentic-AI benchmark report.
How to benchmark CPU sandbox throughput for your agents
Build a test that resembles the tasks your agents actually perform. Keep execution and provisioning as separate measurements, then add an end-to-end view.
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- Define the work unit. Choose a repeatable task such as compiling a fixed repository, running a fixed test suite or completing a scripted tool workflow. Record the exact image, dependencies and input data.
- Declare the sandbox. Name the runtime and version, isolation mode or platform, host processor, virtual CPU count, memory limit and any CPU quota. State whether the environment is warm or cold.
- Measure execution in isolation. Start with ready sandboxes and report completed work per second or minute. Include median and tail task latency when individual operations matter.
- Measure provisioning separately. Record time from create request to a usable environment, create success rate, teardown time and the number of simultaneous requests. Do not label this CPU execution throughput.
- Vary concurrency. Increase simultaneous agents gradually. Watch for queueing, CPU throttling, memory pressure, storage contention and controller or API-server saturation.
- Repeat and report distributions. State test duration, repetitions and percentile results. Averages alone can hide a small group of agents waiting several seconds or minutes.
- Run component-specific tests. Use a CPU-heavy case, a system-call-heavy case, a filesystem case and a network case. This reveals which path changes when you switch runtimes or platforms.
- Calculate end-to-end completion. Include request arrival, provisioning, execution, result transfer and teardown for a realistic agent mix. Explain the proportion of cold starts and reused sandboxes.
Comparison axes that matter more than one headline number
| Axis | Question to answer |
|---|---|
| Execution throughput | How much useful work does one ready sandbox complete per unit of time? |
| Provisioning latency and rate | How quickly can the controller make environments usable, and how does that change as concurrency rises? |
| Tail behavior | What do p95 and p99 creation and task times look like during bursts? |
| Compatibility | Do the agent’s languages, debuggers, processes, syscalls, filesystems and network features work without changes? |
| Resource density | How many concurrent sandboxes fit before CPU, memory, storage or controller limits cause contention? |
| Isolation and policy | Does the isolation model meet the threat model while allowing the required kernel interfaces and performance? |
A platform that wins a CPU loop may lose on short-lived tool calls if creation overhead is high. Conversely, a runtime with extra mediation may be the better choice when isolation requirements outweigh a modest cost on syscall-heavy tasks. Make that trade-off explicit instead of producing a single overall rank.
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Diagnosing a slow agent run
High time before code starts
Inspect image pulls, dependency setup, admission checks, controller queues and API-server pressure. Compare cold and warm starts and record create-to-ready latency under the same concurrency. If provisioning is the limit, a faster CPU microbenchmark will not fix the user-visible delay.
Fast startup but slow computation
Check CPU quota, vCPU placement, host oversubscription and throttling. Confirm that the task is actually CPU-bound rather than waiting on files, network responses or child processes. A ready sandbox can still be starved when many agents share the host.
CPU benchmark is healthy but real tasks regress
Profile system calls, process launches, metadata operations, storage waits and network policy checks. The gVisor performance model predicts that boundary-crossing work can behave differently from raw instruction execution. Test the same workload with realistic data and dependencies rather than relying on a synthetic loop.
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Look for burst queues, garbage collection, image-cache misses, noisy neighbors and controller limits. Report percentile results and concurrency at the inflection point. An acceptable average can conceal unacceptable waits for a minority of agent requests.
Interpreting throughput for capacity planning
Use separate capacity budgets for the controller and for sandbox execution. Provisioning capacity determines how many new environments can become ready during a burst; execution capacity determines how much work those environments can process once ready. Model both against the agent’s arrival rate and task duration.
For predominantly short tasks, reducing cold starts, reusing safe warm sandboxes and avoiding unnecessary teardown may improve completion rate more than selecting a processor with a higher CPU score. For long CPU-heavy jobs, sustained vCPU capacity and fair scheduling matter more. For mixed workloads, measure the actual task proportions and size for the slowest relevant percentile rather than the average alone.
Common mistakes to avoid
- Calling sandbox creations per second “CPU throughput.”
- Comparing a warm execution test with a cold-start test and treating the result as an apples-to-apples ranking.
- Quoting a compatibility percentage as a performance penalty.
- Omitting runtime platform, CPU quota, memory limit or concurrency.
- Using a vendor’s provisioning result to predict every agent workload.
- Reporting only averages when burst behavior and tail latency determine user experience.
- Assuming a raw CPU benchmark covers filesystem, network and system-call overhead.
A practical reporting template
For each result, publish one sentence that identifies the task, runtime and version, isolation platform, host and allocation, cold or warm state, concurrency, duration and statistic. For example: “A fixed compilation task completed X builds per minute in warm sandboxes with Y vCPUs and Z GiB memory at N concurrent workers; p50 and p99 task times were reported.” Replace the variables with measured values—never infer them from a provisioning chart.
Then provide a separate provisioning statement describing create-to-ready latency, concurrency and teardown. This format lets readers determine whether a result applies to their agents instead of mistaking one stage of the lifecycle for the whole system.
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