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admin c46ff918a2 local-horse born: $20k local agentic-coding builds priced (workhorse vs feral cats), model tiers, DRAM-crisis gotcha
Rumination 2026-07-17, not a purchase order. Front-door README + NOTES N-1..N-4 + BUILDS.md.

[ezra]
2026-07-17 11:02:54 -04:00

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# local-horse — findings log
Running, numbered (`N-1`, `N-2`, …). Each entry = something we **learned and want to keep**: a signature,
a gotcha, a decision, a constraint — the stuff we'd otherwise re-derive. NOT the session blow-by-blow (that
lives in the weave thread); this is the distilled, durable record a fresh hand reads to catch up fast.
Newest at the bottom. When a truth changes, **update every place it lives** — a stale note is worse than none.
---
## N-1 (2026-07-17) — Origin: the K3 repricing that started this
Kimi K3 dropped 2026-07-16 (Moonshot AI, 2.8T-param open MoE, weights due **2026-07-27**; API $0.30/M
cached-in · $3/M in · $15/M out). Repricing the adventure game's real build tokens (10 sessions,
2026-07-05..17, summed from `~/.claude/projects/-home-obx/*.jsonl`, deduped by message id):
- **1,147M cache-read** + 18M uncached-in + 3.5M out → **≈ $451 at K3 rates** (±30%, session-level
granularity) vs ≈ $1,307 at Claude list prices. Actual marginal cost: $0 (subscription).
- The load-bearing fact: agentic coding is **~99% context re-reads, ~1% output**. Cached-input price
(cloud) / prefill speed + KV-cache persistence (local) dominate everything.
- K3 itself is never local: ~1.4TB weights at 4-bit. "Local like that" = best open model that fits a box.
## N-2 (2026-07-17) — The open-model landscape, mid-2026
- **GLM 5.2** (Zhipu, MIT, released 2026-06-13): first open model to beat GPT-5.5 on SWE-Bench Pro
(62.1; Terminal-Bench 2.1: 81.0). Current open coding champ. **Exact memory footprint unverified — check
before sizing hardware.**
- **Qwen3-Coder 480B-A35B**: best downloadable coder with known footprint (~270GB @ 4-bit; 35B active).
The 80B-A3B variant ≈ 96% of the quality at single-workstation size.
- **Kimi K2.6**: strongest for agentic *stability* (consistent tool calling, recoverable failures). ~1T
params — barely/slowly fits the workhorse at aggressive quant.
- **The lag law** (jhestyr's intuition, confirmed): open/distilled models run ≈ **12 months behind
frontier**, and the lag is stable. A $20k box today ≈ mid-2025 frontier (Sonnet-4-era agentic coding).
K3 weights (07-27) will spawn distills sized for the workhorse envelope.
- Capability ceiling, honestly: scoped features / refactors / tests / review on a known repo = credible.
Long-horizon overnight autonomy, judgment, Opus/Fable-tier storekeeping = not at any local price.
## N-3 (2026-07-17) — GOTCHA: the 2026 DRAM crisis broke the classic build
Server DDR5 RDIMM prices up **>400% since mid-2025** (64GB: ~$255 Q3'25 → >$900 Q1'26 → $1,2002,300
mid-2026; SK Hynix sold out through 2026, fabs pivoted to HBM). Consequences:
- The classic "EPYC + 768GB DDR5 + one big GPU" build went from ~$16k to **$2842k**. Dead at $20k.
- Workaround: drop to **used Milan + refurb DDR4-3200** (~half the bandwidth, ~1/6 the price) — see BUILDS.md.
- **Used 3090s got *relatively* cheaper** (GDDR6X didn't spike) — the multi-card rig gained ground.
- RTX Pro 6000 Blackwell 96GB: Nvidia list jumped 55% in 16 months to $13,250; street/Max-Q hunts lower.
- Relief not expected before **2027**. Waiting is a legitimate strategy; re-price at buy time.
## N-4 (2026-07-17) — Decision lean (not a decision)
Ezra's recommendation if/when the itch turns real: **the workhorse** (Milan + 512GB DDR4 refurb + one
RTX Pro 6000 Max-Q, ~$1519k) over the 8×3090 rig — a smarter model that's slower wastes fewer turns
than a fast one that circles. Quiet, <1kW, 120V, racks by the Mill, and the skeleton upgrades (RAM,
second card slot) without replacing bones. Integration shape: headless Debian, llama.cpp/vLLM serving an
OpenAI-compatible endpoint on the tailnet; per [[mill-is-home-for-workloads]] it's a Mill annex, not a
new species. Sensible trigger to revisit: **after 2026-07-27** (K3 weights + first distills) or when
DDR5 unclenches.