SLA-first GPU capacity sizing for a serving deployment: given a tokens-per-minute (TPM) target AND the interactivity SLA (output tokens/s/user), compute the pods and GPUs needed from a model's measured tok/s/user-vs-concurrency curve. Sizing MUST start from the tok/s/user SLA, not the TPM number alone: the SLA picks the concurrency, which sets per-pod throughput, which sets the GPU count. Also translates validated optimizations into delta-GPUs at the SLA-pinned concurrency. Backed by the standalone capacitysizing.py tool. Use to answer "how many GPUs for N TPM", "size this for the customer", "pods for the SLA", or to sanity-check a sizing. Triggers on "capacity sizing", "how many GPUs", "how many pods", "tokens per minute sizing", "TPM target", "size for the customer", "interactivity SLA", "tokens per second per user", "GPU count for throughput", or any combination of "size / capacity / how-many" with "GPU / pod / TPM / SLA / tok-per-user / concurrency".
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryEngineeringStars0
About this skill
SLA-first GPU capacity sizing for a serving deployment: given a tokens-per-minute (TPM) target AND the interactivity SLA (output tokens/s/user), compute the pods and GPUs needed from a model's measured tok/s/user-vs-concurrency curve. Sizing MUST start from the tok/s/user SLA, not the TPM number alone: the SLA picks the concurrency, which sets per-pod throughput, which sets the GPU count. Also translates validated optimizations into delta-GPUs at the SLA-pinned concurrency. Backed by the standalone capacitysizing.py tool. Use to answer "how many GPUs for N TPM", "size this for the customer", "pods for the SLA", or to sanity-check a sizing. Triggers on "capacity sizing", "how many GPUs", "how many pods", "tokens per minute sizing", "TPM target", "size for the customer", "interactivity SLA", "tokens per second per user", "GPU count for throughput", or any combination of "size / capacity / how-many" with "GPU / pod / TPM / SLA / tok-per-user / concurrency".
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SLA-first GPU capacity sizing for a serving deployment: given a tokens-per-minute (TPM) target AND the interactivity SLA (output tokens/s/user), compute the pods and GPUs needed from a model's measured tok/s/user-vs-concurrency curve. Sizing MUST start from the tok/s/user SLA, not the TPM number alone: the SLA picks the concurrency, which sets per-pod throughput, which sets the GPU count. Also translates validated optimizations into delta-GPUs at the SLA-pinned concurrency. Backed by the standalone capacity_sizing.py tool. Use to answer "how many GPUs for N TPM", "size this for the customer", "pods for the SLA", or to sanity-check a sizing. Triggers on "capacity sizing", "how many GPUs", "how many pods", "tokens per minute sizing", "TPM target", "size for the customer", "interactivity SLA", "tokens per second per user", "GPU count for throughput", or any combination of "size / capacity / how-many" with "GPU / pod / TPM / SLA / tok-per-user / concurrency".
About this listing
Category
Engineering
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI