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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 — the two priced builds (as of 2026-07-17)

⚠️ Prices dated 2026-07-17, ±15% on used parts and worse on RAM/GPU (see NOTES N-3). Re-price before buying. Budget target: ~$20k. Constraint: no Mac. Both shapes serve an OpenAI-compatible endpoint on the tailnet (llama.cpp / vLLM, headless Debian, Dockge like everything else).


Build 1 — The Workhorse (one big card + cheap capacity)

Quiet, <1kW under load, standard 120V outlet, racks next to the Mill. The pick (NOTES N-4).

Part Spec Est. price (2026-07-17)
GPU RTX Pro 6000 Blackwell Max-Q 96GB (300W blower, 1.8TB/s) $9,50013,000
CPU EPYC 7763 used (64c Milan, 8-ch DDR4; full 8 CCDs = full bandwidth) ~$1,200
Board Supermicro H12SSL-i ~$650
RAM 8× 64GB DDR4-3200 RDIMM refurb (512GB, ~205GB/s) ~$2,0002,800
Storage 4TB NVMe ~$450
Chassis/PSU 4U + 1300W + cooler + fans ~$900
Total ~$15k19k

What it runs (MoE trick: hot path/attention in VRAM, cold experts in system RAM):

Model Fit Speed (est.) Capability, plainly
GPT-OSS-120B (5B active) Entirely in VRAM @ 4-bit 100+ tok/s Reliable daily hand — summaries, scripts, small fixes. ~Sonnet-3.5 tier.
Qwen3-Coder 480B-A35B @ 4-bit (~270GB) Hybrid VRAM+RAM ~1525 tok/s The prize. Real agentic coding on a known repo. Sonnet-4-era.
GLM 5.2 quantized Hybrid (footprint unverified) similar Current open SWE-Bench champ; same tier or a notch up.
Kimi K2.6 (1T) @ Q3 Barely (608GB total, tight) single digits Party trick, not a daily driver.
K3 (2.8T) No Cloud only. Its distills should land in this envelope.

Build 2 — The Feral Cats (8× used RTX 3090, 192GB VRAM)

Everything model-resident → prefill screams. Also screams literally; needs a 240V circuit (~3kW load), ~150W combined idle 24/7, eight points of used-silicon failure.

Part Spec Est. price (2026-07-17)
GPUs 8× used RTX 3090 24GB (~$850 ea; GDDR6X dodged the DRAM spike) ~$6,800
CPU EPYC 7402 used (Rome — just needs the PCIe lanes) ~$300
Board ASRock Rack ROMED8-2T (7× PCIe x16) ~$700
RAM 256GB DDR4 refurb ~$1,000
Risers/frame Bifurcation risers + open frame or 4U conversion ~$500
Power 2× 1600W PSU + sync board, 240V ~$650
Storage/misc NVMe, fans, zip ties, regret ~$550
Total ~$10.5k12k
Model Fit Speed (est.) Capability, plainly
Qwen3-235B-A22B @ 4-bit (~130GB) Fully in VRAM — the rig's sweet spot fast decode AND prefill Strong all-rounder one tier below Coder-480B. Best agentic feel per dollar here.
GPT-OSS-120B Fully in VRAM, headroom blazing Same as workhorse, faster.
70B-class dense (Llama/Qwen) Trivially blazing Helper tier.
Qwen3-Coder 480B / GLM 5.2 Doesn't fit at useful quant offload defeats the rig's point The one thing the workhorse does that this can't.
K2.6 / K3 No

The trade in one sentence

Workhorse reaches the best open coding models slowly-but-surely and behaves like an appliance; the cats run one tier down blisteringly fast for half the money, paid for in a 240V run, fan roar, and jank. For agentic loops (99% context re-reads — NOTES N-1) all-VRAM prefill genuinely matters, but model ceiling matters more: a smarter slow model wastes fewer turns than a fast one going in circles.

Why one big card and not 8 (the physics, kept for re-derivation)

  1. Power/noise: 8×350W = 2.8kW load — over a 15A/120V circuit's ceiling; plus ~150W idle, forever.
  2. Interconnect tax: split models cross PCIe (~32GB/s) every token vs 1.8TB/s on-card; consumer cards have no P2P/NVLink (disabled on 4090+). Multi-GPU wins at batch throughput (many users), not single-user latency — and the counter has one user.
  3. MoE changed the buy: ~35B active params need one fast GPU + cheap capacity, not 8 GPUs of compute.

Sources (as read 2026-07-17)

  • Open-model landscape: digitalapplied.com (models↔hardware matching) · llmconfigurator.com (local coding report) · modal.com (SWE-bench open models) · mindstudio.ai (agentic open-source 2026)
  • Hardware/pricing: videocardz.com (RTX Pro 6000 list $13,250) · thundercompute.com (build pricing) · corewavelabs.com + pcserverandparts.com (DRAM crisis) · tomshardware.com RAM price index · memory.net
  • K3: venturebeat.com · cnbc.com · marktechpost.com · simonwillison.net · platform.kimi.ai quickstart