TinyTitan Datacenter 1.1

TinyTitan Datacenter 1.1

ttd 1.1 is the first release under this project's own identity, and the number is its own.
TinyTitan Datacenter is a dedicated repository - not a fork, with no upstream - built from scratch
and licensed under MIT, and its release line starts from v1.0.0 rather than from any number
inherited with the code. The version is 1.1, two components: there is no patch component in this
project.

What is in it

The repository ships one runtime: a native Swift and Metal engine that streams routed experts from
SSD so a model larger than RAM still runs, plus the cluster layer that runs it across machines.
Single node, measured. Four Mac minis, one binary (md5 ec6710cd... on all four), 128 tokens at
temperature 0, prompt "The capital of France is":
| node | tok/s | | --- | --- | | node1 | 7.935 | | node2 | 7.937 | | node3 | 7.884 | | node4 | 7.146
/ 7.448 / 7.396 |
Mean 7.73, and every machine produces the same text.
Four nodes, measured. A four-stage layer pipeline runs one model across all four machines and
produces the reference text at 6.016 tok/s at 128 tokens. It is correct and balanced within 9% -
and it is slower than a single node, which this release states rather than dresses up.

What this release does not claim

21 tok/s is not reachable on this hardware as configured, and the reason is measured rather than
asserted:

• the step is 141.6 ms/token: 47% expert I/O and 50% GPU wait. Those two phases are
  serial, not overlapped - the profiler's buckets sum to the step (66.5 + 71.3 + 3.8 = 141.6), and
  taking 42.5 ms/token of expert-I/O await out of a run takes 42.8 ms of step with it, a
  ratio of 1.007. Correction: an earlier draft read "the expert I/O is already fully
  overlapped" on the strength of exposed_io reporting 0.0 ms on every node; that counter does
  not answer whether the I/O adds to the step, and the arithmetic says it does. Found after v1.1
  shipped, so the claim is withdrawn here;
• a layer pipeline divides memory, not time: the head's period of 166.2 ms/token sits at
• the serial prediction (the sum of the stage times, 194.8 ms) and nowhere near the pipelined
• one (48.7 ms);
• every single-node tuning axis is at its optimum - prefetch depth, the prefetch ring, and the
• expert cache, where 40 slots is the best of seven points and 64 and above collapse into
• swap;
• the wire is 1 GbE: 118 MB/s and a 765 us round trip, and UDP measures the same as TCP, so
• it is the link and not the protocol. Pooling expert residency is **8.6x slower than local
• disk**, and tensor parallelism spends 61 ms of synchronisation against a 47.6 ms target.
  The route to the target is a faster link, not more engine work. The Thunderbolt and 10GbE ports
  on these machines report status: inactive; connected, an RTT near 70 us puts the step near 26.6
  ms.

Verification

• The four single-node runs and the four-stage chain above, each with its timing footer.
• swift test --no-parallel on the runtime.
• The release gates: force-cast ok, func-length ok (0 baselined, 0 new, 2186 scanned),
• unchecked-sendable ok, arch-path ok.
• A clean scratch release build. The archive is:
  TinyTitan_Datacenter-1.1-macos-arm64.tar.gz sha256:
  eb154f05b485c03a57d41f887d63f1906e7f447fb27dda1b5b6274a3a4864bfa TinyTitan_Datacenter-1.1-macos- arm64.tar.gz size: 25830004 bytes
• (Both fields are filled in at publish time. A clean rebuild is not byte-reproducible, so a
• digest or size quoted here would go stale the moment the archive was rebuilt.)
• No model, dataset or dependency was fetched to make anything pass.

Not checked, and named here as the gate requires

• Every golden baseline is NOT CHECKED. No install was present under models/ on the
• machine that cut this release, and none may be fetched to change that:
• ornith-8, ornith-4, qwen36-4, qwen36-8, qwen38-4, qwen38-8, agentworld-4,
• agentworld-8, katcoder-4, katcoder-8, qwen35-2b-4, qwen35-2b-8, qwen35-4b-4,
• qwen35-4b-8, qwen35-9b-4, qwen35-9b-8. **This release therefore ships no golden-baseline
• evidence.** The benchmarks quoted above were measured through the CLI against an install
• outside models/, which is a measurement, not a golden gate.
• The converter-expert-order gate reports SKIP: No module named 'numpy' — the converter's
• dependencies are unavailable here, so that gate is not checked either.