System One runs a single forward pass — no token-by-token generation
tev1:0.8bOllama v0.35+Local GPU
Input State
Decision Questions
Decision Output
Run a scenario to see results…
Raw Response
—
System One Execution Stats (This Run)
1
Forward Passes
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Latency (ms)
1
Output Tokens
Head-to-Head Benchmark
Compare System One (local) vs cloud LLM on the same decision task — tokens update live each run
System One (tev1)
Local · GPU · Single forward pass
FREE $0
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Latency ms
1
Output Tokens
1
Forward Passes
Token Strip
Run benchmark to see tokens…
Cloud LLM
Remote · Autoregressive · N steps
💰 PAID
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Latency ms
—
Output Tokens
—
Forward Passes
Token Strip (each = 1 step)
Run benchmark to see tokens…
Comparison Summary
Metric
System One (tev1)
Cloud LLM
Winner
System One vs Autoregressive LLM
Why 1 Step Beats 350 Steps
Traditional LLMs generate decisions token-by-token — each token is a separate GPU forward pass.
System One collapses this into a single calibrated probability distribution.
⚡
System One (tev1)
Single forward pass decision engine
1️⃣
Input arrives — context + decision schema loaded
🔁
One forward pass — full attention over all inputs simultaneously
📊
Logit projection — calibrated probability over each choice class
✅
Done. Structured JSON output with confidence scores
1
GPU forward passes
1
Token generated
~28ms
Typical latency
$0
Cost (local GPU)
🐢
Autoregressive LLM
Token-by-token generation loop
1️⃣
Prompt constructed — system prompt + user message built
🔄
Reasoning tokens — LLM "thinks" through the problem step by step
🔄
Output tokens — JSON response generated one token at a time
⚠️
Parse + validate — may hallucinate invalid schema, needs retry
~350
GPU forward passes
~120
Tokens generated
~1800ms
Typical latency
~$0.001
Cost per call
Visualise the Step Gap
System One: 1 step
Cloud LLM: — steps
Tip: Run a Benchmark to see real numbers from your last call. Currently showing: no live data yet
What Those Tokens Actually Are
🟡 Reasoning Tokens (hidden thinking)
The model "thinks" before answering. These tokens are counted and billed but you never see them. Each one is a full GPU forward pass.
LetmeanalyzethisticketcarefullyThepriorityappears
🔵 Output Tokens (the JSON you wanted)
After all the thinking, the model finally writes the JSON answer — also one token at a time, one forward pass each.
{"priority":"high","route":"billing"}
⚡ System One: just this
DECISION_COMPLETE
One token. One forward pass. Structured probability distribution over all choices simultaneously.
Data Pipeline
Watch real JSON flow through each stage, then see the actual output from both systems