vh
33433e0d1e
Ran the loss path on the real checkpoint on GPU0 with synthetic tokens.
The arithmetic held for parameter counts and was badly wrong for
activation memory.
naive CE bsz1 seq 8192 81.93 GiB
naive CE bsz1 seq16384 OOM
chunked CE bsz1 seq16384 65.66 GiB
chunked CE bsz2 seq16384 79.71 GiB <- the run config
chunked CE bsz4 seq16384 OOM
The marginal cost of an extra 16,384-token sequence is ~14 GiB, not the
~5 GiB estimated: the estimate modelled gradient checkpointing as
storing layer inputs plus a modest recompute peak, and the real MoE
recompute peak with top-8-of-128 routing and its scatter/gather buffers
is far heavier. Dense-model intuition does not size an MoE run.
Two predictions landed exactly — 205 target modules and 74,342,400
trainable params at r64 — which is why the rest of the model of the
thing is still worth trusting.
The headline is that chunked CE at seq 16384 costs 16 GiB less than
naive CE at seq 8192, so chunking is what makes brokkr's 16384
recommendation reachable rather than an optimisation on top of it.
max_seq_len moves 8192 -> 16384 on his truncation finding: the cap
drops 6.2% of samples but 22.4% of tokens, concentrated entirely in
dialogue, which is 60% of the mix.
Also records the four harness changes this required (eitri-smithy
62b556b), including the inert-adapter trap: without
enable_input_require_grads() alongside gradient checkpointing on a
frozen base, no gradient reaches the adapters, every one stays at its
initialisation, and the run completes successfully having learned
nothing.
docs/
Navigation map for the documentation tree. New session? Read
orientation.md first — it's the narrative overview
of the fleet, backup architecture, governing principles, and gotchas,
and it points at everything else.
Tree
docs/
├── orientation.md # start here — fleet overview + where-to-look guide
├── runbooks/ # ops runbooks (recovery, deployment phases)
│ ├── disaster-recovery.md
│ ├── nh3-prune-ritual.md
│ └── pbs-deployment.md
└── pfi/ # PFI-specific reference (services, models, VMs)
├── docker-stack.md
├── model-list.md
├── proxmox-vms.md
├── recommended-model-settings.md
├── vm-102-matrix-appservice.md
└── vm-102-matrix-synapse.md
What goes where
runbooks/— step-by-step ops procedures. Anything you'd reach for during an incident or while standing up new infrastructure. Examples: disaster recovery (blast-radius tiers + restoration steps), PBS deployment (9-phase rollout). New runbook → new file here.pfi/— PFI-specific reference material that's too narrow for the top-level CLAUDE.md but doesn't change incident response. AI model inventory, recommended inference settings, Matrix bridge config, Proxmox VM map. New stable reference → new file here.- Top-level (
docs/orientation.md,docs/README.md) — narrative guides about the workspace itself, not about specific infra.
Cross-references
- Fleet topology + servers table: top-level
CLAUDE.md. - Open work + recent milestones: top-level
STATUS.md. - Durable cross-session facts:
~/.claude/projects/-home-lkraven-development-eshpfi-management/memory/.
Conventions
- Markdown, GitHub-flavored. CommonMark renders fine in most viewers.
- File names are lowercase-kebab-case, descriptive. No dates in filenames — git history covers that.
- One topic per file. If a file grows past ~500 lines, look for a natural split before adding more.
- No checked-in binaries or checksums. Build/release artifacts belong
in a build pipeline or
tools/, notdocs/.