slosizer 0.2.0

Capacity planning for reserved LLM throughput: latency, headroom, and synthetic workload simulation.

slosizer: Right-size reserved LLM capacity Based on SLO

slosizer is a small Python package for sizing reserved LLM capacity against either a throughput objective or a latency SLO.

It takes request traces, converts them into provider-specific capacity work, simulates queueing under bursty arrivals, and tells you how many reserved units you should buy plus how much slack capacity you are likely to carry.

The package is built for the extremely normal situation where:

• you know your request shape better than your vendor calculator does,
• you care about p95 or p99 latency, not just average throughput,
• and you do not want your capacity plan to be a sacred spreadsheet that nobody trusts.

What problem does this solve?

Reserved-capacity systems like GSU/PTU are fundamentally throughput constructs, but production teams usually care about latency SLOs, burst risk, and headroom.

slosizer gives you one place to:

1. ingest raw request logs into a canonical RequestTrace,
2. turn requests into provider-specific adjusted work,
3. plan capacity for either:
   • throughput: control overload probability or required-unit percentile,
   • latency: satisfy p95/p99 queue-aware latency targets,
4. quantify:
   • spare capacity,
   • overload probability,
   • expected overflow,
   • optimization benefit.

What does "ingest" mean here?

It just means map your raw logs into the clean columns the planner expects.

If your real data has columns like timestamp, prompt_tokens, completion_tokens, reasoning_tokens, and cache_hit_tokens, the package standardizes that into a canonical RequestTrace with:

• arrival_s
• class_name
• input_tokens
• cached_input_tokens
• output_tokens
• thinking_tokens
• max_output_tokens
• observed_latency_s

That is all. No incense. No chanting.

Quickstart

1) Create the environment with uv

uv sync --all-groups

2) Run the shipped synthetic demo

uv run python examples/quickstart.py

This writes:

• examples/output/comparison.csv
• examples/output/latency_vs_capacity.png
• examples/output/required_units_distribution.png
• examples/output/scenario_benefit.png
• examples/output/percentile_tradeoff.png

3) Run the checks

uv run pytest -q
uv run ruff check src tests examples
uv run ruff format --check src tests examples
uv run deptry .
uv run vulture

Install and use it on your own trace

Minimal latency-oriented example

import pandas as pd
import slosizer as slz

df = pd.read_csv("requests.csv")

trace = slz.from_dataframe(
    df,
    schema=slz.RequestSchema(
        time_col="timestamp",
        class_col="route",
        input_tokens_col="prompt_tokens",
        cached_input_tokens_col="cached_prompt_tokens",
        output_tokens_col="completion_tokens",
        thinking_tokens_col="reasoning_tokens",
        max_output_tokens_col="max_output_tokens",
        latency_col="latency_s",
    ),
    provider="vertex",
    model="gemini-2.0-flash-001",
)

profile = slz.vertex_profile("gemini-2.0-flash-001")

result = slz.plan_capacity(
    trace,
    profile,
    slz.LatencyTarget(
        slz.LatencySLO(
            threshold_s=1.5,
            percentile=0.99,
            metric="e2e",
        )
    ),
)

print(result.recommended_units)
print(result.metrics)

Throughput-oriented example

import slosizer as slz

trace = slz.make_synthetic_trace(seed=42)
profile = slz.vertex_profile("gemini-2.0-flash-001")

result = slz.plan_capacity(
    trace,
    profile,
    slz.ThroughputTarget(
        percentile=0.99,
        max_overload_probability=0.01,
        windows_s=(1.0, 5.0, 30.0),
    ),
)

print(result.recommended_units)
print(result.slack_summary)

Azure PTU example

Azure support is calibration-first: you seed a profile from the Azure calculator and benchmark results, then use the same planning machinery.

import slosizer as slz

profile = slz.azure_profile(
    "gpt-4.1",
    throughput_per_unit=12000.0,
    input_weight=1.0,
    output_weight=4.0,
    thinking_weight=4.0,
)

What data do you need?

You can start with only these three fields:

• timestamp
• input_tokens
• output_tokens

You get better plans when you also provide:

• cached_input_tokens
• thinking_tokens
• max_output_tokens
• class_name
• latency_s

See the concrete schema guide in docs/data-requirements.md.

There are also example input files in:

• examples/input/synthetic_request_trace_baseline.csv
• examples/input/synthetic_request_trace_optimized.csv

Built-in provider support

Vertex GSU

The package ships a small built-in registry for a handful of Vertex models, including:

• gemini-2.0-flash-001
• gemini-2.0-flash-lite-001
• gemini-2.5-flash
• gemini-2.5-flash-lite
• gemini-2.5-pro
• gemini-3.1-flash-lite-preview

Azure PTU

Azure PTU support is user-calibrated on purpose. The package gives you the same planning engine, but you provide the model-specific PTU profile from your calculator + benchmark loop.

See docs/provider-adapters.md.

Synthetic demo: what it shows

The repo ships with a fake but bursty workload containing three classes:

• chat
• rag
• reasoning

The optimized variant simulates:

• tighter prompts,
• more caching,
• shorter outputs,
• lower thinking-token budgets.

That lets you inspect two things immediately:

1. Optimization can reduce reserved-capacity needs.
2. Planning for stricter percentiles usually increases slack capacity.

Snapshot of the current synthetic outputs

scenario	objective	target	recommended units	avg spare fraction (1s)	overload probability (1s)	achieved latency quantile
baseline	latency	p95 <= 1.5s	5	0.718	0.030	1.315s
baseline	latency	p99 <= 1.5s	7	0.794	0.006	1.428s
baseline	throughput	p99 units, overload <= 1%	7	0.794	0.006	-
optimized	latency	p95 <= 1.5s	4	0.713	0.032	1.157s
optimized	latency	p99 <= 1.5s	5	0.766	0.012	1.278s
optimized	throughput	p99 units, overload <= 1%	6	0.804	0.005	-

These numbers are synthetic. They are there to show the mechanics, not to cosplay as your production traffic.

Output plots

Latency vs provisioned capacity

Distribution of required reserved units

Optimization benefit

Slack trade-off

Repo map

• docs/formalization.md: generic throughput/latency model
• docs/data-requirements.md: what columns you need and why
• docs/provider-adapters.md: how GSU/PTU adaptation works
• docs/examples.md: the synthetic walkthrough
• examples/quickstart.py: reproducible demo script

Caveats

• The queue model is intentionally simple: FCFS fluid queueing, not a perfect service simulator.
• Built-in Vertex profiles are text-centric. Multimodal traffic needs more columns and weights.
• Azure PTU math is workload-sensitive, so the package does not fake vendor-authoritative PTU values for you.
• If you do not have a latency column, the package falls back to a simple token-based baseline latency model. That is a starting point, not gospel.

Name

The package name is slosizer because "how many units do I need, and how much empty air am I buying to hit p99?" is the real question under all the vendor jargon.