GPU Sandbox Model Evaluation: Compare LLM Checkpoints From Chat (2026)

GPU sandbox model evaluation turns "did checkpoint three actually get better" from a half-day of manual setup into one chat message: an agent provisions a GPU, installs an eval harness, scores two or more checkpoints on the same benchmarks, and reports the numbers back in the conversation. Spheron's Agent GPU Sandbox names this as a documented use case, with a specific example prompt and a specific suggested GPU, which makes it worth walking through end to end rather than describing in the abstract.

TL;DR: What Is GPU Sandbox Model Evaluation?

GPU sandbox model evaluation is asking a chat agent, such as Claude Code connected over MCP, to provision a GPU itself, run a standard benchmark suite against two or more model checkpoints, and report the scores side by side in one table, without you opening a terminal.

• Named use case: Spheron's Agent GPU Sandbox feature page lists "evals on open models" with the documented example prompt "Run MMLU and GSM8K on these three checkpoints and compare them."
• Approval: every deploy, Stop, Start and terminate needs explicit approval against a price preview before Spheron's sandbox runs it.
• Verdict: the sweep is built to catch a checkpoint that improved on one benchmark while quietly regressing on another, before that checkpoint ships.

The Prompt: Asking Claude Code to Eval Three Checkpoints

The prompt that triggers this is almost exactly what you'd say to a teammate. Spheron's own documented example on its Agent GPU Sandbox feature page is: "Run MMLU and GSM8K on these three checkpoints and compare them." That's the whole instruction. No flags, no harness name, no mention of which GPU tier to request unless you want to override the default.

What makes that single sentence enough is that the feature page treats the comparison itself as the product, not a workaround assembled from a generic code sandbox: "Run an eval suite across checkpoints or models and get the scores side by side in one table" is the use case's own description, with 1x H100 named as the suggested hardware. You can name the checkpoints by path, by Hugging Face Hub ID, or by training step if they're snapshots from the same run, the way you'd tell a colleague which three commits to diff.

If the checkpoints aren't three separate repos but snapshots inside one fine-tuning run, the agent needs a way to address each step individually, which is where the harness's own checkpoint-selection argument comes in.

How the Agent Picks a GPU and Runs the Eval Suite

A single H100 is enough for this because an eval sweep is a read-only forward-pass workload, not training: it gives the agent headroom to serve a mid-size open checkpoint and batch benchmark prompts against it without sharding across cards. If a checkpoint is large enough to need more memory, the sandbox also supports H200, B200, B300, A100, L40S and RTX tiers, and the agent requests whichever one the prompt names.

Once the GPU is approved, a realistic run installs EleutherAI's lm-evaluation-harness, which the project's own repository describes as including a vLLM backend the maintainers note is "especially faster when splitting a model across multiple GPUs." MMLU and GSM8K are both in that catalog by name. To point the harness at a specific snapshot rather than a model's default weights, it accepts a Hugging Face Hub revision argument, for example revision=step100000.

The eval itself has to run as a background command, so the agent launches it as a background job instead, which Spheron's Agent GPU Sandbox docs allow to run for up to 24 hours per call while streaming output back to the chat. File transfer into the VM is capped at 5MB per file, so the agent pulls weights from the Hub directly on the machine rather than pushing a checkpoint through chat.

Is It Safe to Let an Agent Run Eval Scripts With Sudo?

The sandbox is built to bound what can go wrong, not to pretend nothing can. The agent runs shell commands, including installing the harness and launching the eval, as the login user with sudo, and every one of those commands is logged with its exact text and exit code. Around that access sit several controls documented on the sandbox's own page: every deploy, Stop, Start and terminate shows a price preview and needs your explicit approval before it executes, and the VM opens no inbound port, so nothing outside the agent's own outbound connection can reach it. A per-connection spend limit also caps what an unattended multi-checkpoint sweep can run up if something goes wrong mid-run. Your own SSH key keeps working the whole time, so you can log in and watch the eval harness run directly instead of taking the chat summary on faith.

Reading the Results Back in Chat: What Gets Reported

Only the first 1MB of any command's output returns to the chat, with the full log saved to /var/log/spheron-agent/ on the machine. So what actually lands in the conversation is the harness's own summary table: accuracy per subject or task, one row per checkpoint, rather than a transcript of every graded answer.

That's the table the feature page means when it says checkpoints get compared "side by side in one table." Reading it is closer to reading a diff than a report: the useful signal is the column-by-column movement between checkpoint one, two, and three on the same benchmark, not any single checkpoint's absolute score. The full per-question logs on /var/log/spheron-agent/ are there if a subject's score needs a closer look, and that's also where to check which revision the harness pointed at for each checkpoint.

Catching a Regression Before You Promote a Checkpoint

This is the actual reason the use case exists, not just a novelty of typing a benchmark command into a chat window. Picture a team that fine-tuned a third checkpoint on a new batch of domain data on top of an existing run, heavy on one subject area, with the working assumption that more training on better-curated data makes the newest snapshot strictly better.

A three-way MMLU and GSM8K sweep is built to catch the case where that assumption is wrong in a way a loss curve alone wouldn't show. GSM8K's 1,319 math problems test a different skill entirely from MMLU's multiple-choice recall, so a regression that shows up in only one of the two benchmarks is exactly what a single aggregate score would hide. Reading both scores against the two older checkpoints in the same table is what turns "this checkpoint feels better" into a decision you can defend before it ships to serve traffic.

GSM8K's own dataset card flags that about 1.7% of its problems still produce disagreement among human reviewers checking the answer key, a small but real ceiling on how literally to read a single-point swing between checkpoints on that benchmark alone. That's one more reason to read MMLU and GSM8K together rather than picking whichever one favors the checkpoint you were already leaning toward.

For a narrower comparison, such as deciding whether a SLERP merge of two existing fine-tunes clears the bar instead of training a third model from scratch, Spheron's mergekit guide runs the same kind of eval-sweep logic against that specific decision. And for open-ended tasks that a fixed benchmark like MMLU can't score at all, such as judging which of two summaries reads better, a self-hosted LLM-as-judge pipeline is the better-fit tool than a quick sandboxed accuracy check across a handful of checkpoints.

Cost and Runtime for GPU Sandbox Model Evaluation Sweeps

The sandbox itself carries no separate fee. You pay the GPU instance's normal hourly rate for however long the eval sweep runs, the same rate as renting that GPU directly. Spheron's on-demand H100 runs $3.96/hr as of 08 Nov 2026, and because an eval sweep is pure inference rather than training, it's cheaper per hour of GPU time than the fine-tuning run that produced the checkpoints in the first place.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 08 Nov 2026; other figures reflect benchmark and dataset documentation current as of the sources cited and may have changed. Check current GPU pricing → for live rates.

Runtime scales with how many checkpoints and how many benchmark questions you're running, not with model size alone. A three-checkpoint MMLU plus GSM8K sweep, roughly 15,000 graded prompts per checkpoint across both benchmarks, comfortably fits inside the sandbox's 24-hour background-command ceiling on a single H100. The shell access the sandbox gives you is real shell access, not a packaged eval dashboard: lm-evaluation-harness still has to be installed and driven by the agent itself. For a model trained on the sandbox in the first place, Spheron's LoRA fine-tuning walkthrough covers the training half of the same "chat prompt in, GPU work out" mechanism, and the vLLM vs SGLang benchmark walkthrough runs the throughput-focused sibling of this use case on the same sandbox. It's also a multi-minute GPU workstation rather than a sub-second code runner: Spheron's AI agent code execution sandbox guide covers the CPU-only tools built for instant iteration instead.

Comparing checkpoints before you promote one only works if the agent running the comparison can get its hands on a real GPU without you opening a terminal yourself, which is what the Agent GPU Sandbox is built for.

Connect your agent to the Agent GPU Sandbox →