A living map of
the next builds.
vram.wiki started as a small answer to one busy r/LocalLLM thread. The community decides what comes next — this is the public trail of where the project is going.
Born from the original threadBuild the signal
A sourced, database-backed catalog of real-world local LLM setups, with immutable revisions and atomic publication.
- Neon entries, revisions and suppression-aware projections
- Machine-level hardware cards, timelines and useful references
- Authenticated contributor dashboard and private ownership
- Admin review, staging and publication workspace
Make contributors visible
Contributors choose a separate public identity while keeping GitHub private, and can recover when a review needs more work.
- Opt-in handles and public /u/[handle] profiles
- Public attribution only for opted-in published snapshots
- needs_changes feedback with versioned audit history
- Editable correction and resubmission flow
Connect the workshop
Anonymous form or a scoped agent connection, with the same human review gate for every path.
- Remote MCP with GitHub OAuth, PKCE and revocable access
- Strict proposal contracts, rate limits and idempotent retries
- Anonymous Tally submissions and signed webhook staging
- Connected-agent management and privacy guidance
Reward the practice
Accepted contributions earn visible, evidence-linked recognition instead of raw submission volume.
- Badges, an append-only XP ledger and contributor opt-in
- Enriched contributor profiles and a founding cohort
- Side-by-side setup comparison and shareable cards
- Curated optimizer patterns from real snapshots
Navigate and notify
Easier exploration and a review loop that keeps up, without weakening the human gate.
- Discovery pages by model, runtime and GPU, with context and speed filters
- Transactional email: maintainer alerts and opt-in contributor notices
- A moderation queue with archive/delete and Tally normalization
- A scheduled retry worker for the email outbox
Explore and learn the practice
Pair the community record with the theory behind it, then give the best write-ups a credited home.
- An /explore hub crossing hardware, model, runtime, capacity and task
- A 48-lesson curriculum with progress, XP and learning badges
- A technical glossary and enriched /model pages
- Community papers rendered in full with credit and license
- An evalkit harness presented at /benchmarks
Open the workshop
Meet contributors where they are and record the GPU facts the catalog reasons over.
- Contributor onboarding with public-handle and notification choices
- Per-card GPU records as the capacity authority
- MCP hardened and enabled in production
- Owner-only update flow over immutable revisions
Read the hardware
One entry can describe several models and quants, and the same hardware can be found from several pages.
- Per-model rows: quant, role and per-model measures
- A derived hardware signature view at /rig/<slug>
- Quant and model facets that read the per-model rows
- Cross-links between setups that share hardware
Own the machine
A durable, owned hardware base that outlives one revision: it evolves, carries photos and hosts several use cases.
- A stored machines entity referenced by revisions
- A public /machine/<slug> page with timeline and photos
- A dashboard "My machines" section
- A backfill for already-owned entries only
Ask the catalog
A visitor's own agent can read the catalog, and the path from a hardware check to a real setup is measured and eased.
- Public, read-only MCP tools: search, similar, capacity-for
- "Can I run, with your agent": /can-i-run crossed with /mcp
- Forgiving directory search and a zero-result helper
- A local PostHog behaviour report and the contribution-outcome event
From check to choice
Close the loop between someone checking their hardware and finding the setup that fits.
- Content and outreach for the empty segments (no plain RTX 4080 or M1 Pro setup yet)
- A self-service paper claim and per-snapshot license/consent
- A retention loop: "your setup was cited" notices and per-entry view counts
- A public, versioned dataset export and feeds
Compare the practice
Once the data is deep enough, surface comparisons that respect real conditions instead of pretending every token-per-second number is a benchmark.
- Cross-machine and cross-contributor hardware views
- ROI reports with transparent assumptions
- Evidence-based community rankings
- A long-term time series of local AI practice