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Translate model weights, quantization, context, KV cache and concurrent coding tasks into a GPU/VRAM plan for local OpenHands inference.
Before running commands in production, validate versions, backups, firewall rules and the rollback plan on your own infrastructure.
OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent.
Do not approve the Which GPU for OpenHands? Model and VRAM Guide design merely because every service starts. Keep the GPU inference API behind a private subnet or authenticated gateway to reduce attack surface. Validate the real network and data path against OpenHands Docker Sandbox documentation before production.
Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM. Capacity testing should therefore use representative data and concurrent work on Which GPU for OpenHands? Model and VRAM Guide; idle RAM alone is not a sizing decision.
Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom. For GPU-accelerated workloads, benchmarks are not comparable unless model/data, concurrency and measurement window remain identical.
Keep the model/data, concurrency and measurement window identical across comparisons. Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM. Record failure rate and peak resource usage next to throughput.
Keep the GPU inference API behind a private subnet or authenticated gateway to reduce attack surface.
Access control for Which GPU for OpenHands? Model and VRAM Guide is an architectural input rather than a post-deployment add-on. OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent. Database, worker, runtime or admin ports that do not need public exposure should remain private.
Before purchasing, measure tokens/s, time-to-first-token and task success rate on the same task set.
Use this operation as one release verification point: nvidia-smi --query-gpu=name,memory.total,memory.used --format=csv. Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM. If it fails, validate the rollback point before proceeding.
Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM.
To separate symptoms from root cause in Which GPU for OpenHands? Model and VRAM Guide, record the last change first. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom. Then correlate service logs, dependency health and network reachability on the same timeline.
Before purchasing, measure tokens/s, time-to-first-token and task success rate on the same task set.
Before purchasing, measure tokens/s, time-to-first-token and task success rate on the same task set. Keep configuration, persistent data, secret inventory and restore order as separate runbook items, and review OpenHands Docker Sandbox release guidance before upgrades.
Translate model weights, quantization, context, KV cache and concurrent coding tasks into a GPU/VRAM plan for local OpenHands inference.
Choose Which GPU for OpenHands? Model and VRAM Guide against the actual objective rather than product popularity: Translate model weights, quantization, context, KV cache and concurrent coding tasks into a GPU/VRAM plan for local OpenHands inference. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom. If those conditions are not yet known, start with a smaller PoC.
OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
| Symptom / problem | Likely layer | First verification |
|---|---|---|
| Sandbox starts but workspace is not writable | Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM. | Correlate the relevant service log, dependency health and the last change on one timeline. |
| Agent cannot reach the local model endpoint | Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom. | Measure peak resources, concurrency and disk/network pressure in the same test window. |
| Tool calls produce malformed JSON or timeouts | Keep the GPU inference API behind a private subnet or authenticated gateway to reduce attack surface. | Verify public/private ports, authentication, TLS and secret scope from outside in. |
| GPU is available but task success remains low | Before purchasing, measure tokens/s, time-to-first-token and task success rate on the same task set. | Check version, config diff, persistent data and the rollback point together. |
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
Translate model weights, quantization, context, KV cache and concurrent coding tasks into a GPU/VRAM plan for local OpenHands inference.
OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent.
Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
Keep the GPU inference API behind a private subnet or authenticated gateway to reduce attack surface.
Before purchasing, measure tokens/s, time-to-first-token and task success rate on the same task set.
Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
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nvidia-sminvidia-smi --query-gpu=name,memory.total,memory.used --format=csvcurl http://127.0.0.1:8000/v1/modelsApproximate VRAM planner for model weights
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow.
OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
OpenHands itself does not inherently require a GPU; GPU demand comes from the local inference model connected to the agent.
Keep the GPU inference API behind a private subnet or authenticated gateway to reduce attack surface.
Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.
Before purchasing, measure tokens/s, time-to-first-token and task success rate on the same task set.
Parameter count alone is misleading; quantization and context can make the same model consume very different VRAM.
No. It is an initial planning estimate. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom. Validate the final decision against the real workload.
Beyond install commands, this guide covers architecture, capacity, security, troubleshooting and production operations as one workflow. Leave headroom beyond model weights for KV cache, runtime workspace and framework overhead; context and concurrency reduce available headroom.