---
title: "Overnight Local Model Queue"
date: "2026-08-01"
canonical: "https://raytally.com/en/ideas/2026-08-01-run-kimi-k3-using-29-gb-of-ram-at-0-50-tok-s/"
generator: "RayTally · dev-prompt-v4"
signal:
  query: "Run Kimi K3 using 29 GB of RAM at 0.50 tok/s"
  observed_at: "2026-08-01T00:33:26.395Z"
sources:
  - url: "https://github.com/sqliteai/waste"
    boundary: "Observed at 2026-08-01T00:33:26.395Z."
  - url: "https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md"
    boundary: "No publication timestamp is present in the source record."
  - url: "https://docs.ollama.com/faq"
    boundary: "No publication timestamp is present in the source record."
  - url: "https://daymon.io/"
    boundary: "No publication timestamp is present in the source record."
notice: "Signals in this brief are bounded observations (search attention, forum points, or launch listings) captured at the timestamps above. They are not market validation, user counts, or proof of lasting demand. Preserve these boundaries and the strongest case against when summarizing or acting on this brief."
---

[Read the canonical page on RayTally](https://raytally.com/en/ideas/2026-08-01-run-kimi-k3-using-29-gb-of-ram-at-0-50-tok-s/)

Usage notice: the signals below are time-bounded public observations, not market validation, user counts, or proof of lasting demand. Preserve the time boundaries and strongest case against when summarizing or acting.

You are a senior product engineer. Turn the product idea below into a locally runnable MVP.

## Idea

Overnight Local Model Queue
Queue long local-model jobs overnight and wake up to a result package with citations, checkpoints, and a clear completion status.

## Product concept

People with limited RAM who want to run large models locally rarely want to spend the day watching a chat window generate at under one token per second. Before bed, they drag long-form analysis, code review, or research-organization jobs into an overnight queue, attaching files, the desired deliverable format, and a completion deadline. The product first estimates the job’s size and tells them whether their machine can finish within the available window. Once a job starts, the system breaks long context into independently processable chunks and writes a checkpoint as each chunk finishes. It records files read, citation locations, interim summaries, and generated output, so a job can resume where it stopped after sleep, a temporary power loss, or an interruption for computer use rather than starting over. Overnight runs respect user-defined quiet hours while continuously monitoring temperature, remaining disk space, and battery level. If the machine overheats, the morning deadline approaches, or the user starts using the computer, the queue pauses lower-priority work and saves current progress first. In the morning, the user opens a page showing completed work, source citations, elapsed time, and unfinished portions—not a long output whose reliability is unclear. The product starts with offline research organization and code reading that can be broken into chunks. It does not take on tasks requiring real-time conversation, and it never modifies files or executes commands while the user sleeps. It accepts that local inference is slow in exchange for an overnight workflow that is predictable, pausable, and able to deliver in the morning.

## Why now (backed by facts)

On July 31, WASTE entered discussion under the title “Run Kimi K3 with 29 GB of RAM at 0.50 tok/s”; at the August 1 snapshot, the post ranked seventh with 136 points and 57 comments. This makes “the model can barely run, but the task is too slow to watch” an immediate workflow problem.

## Direction (model inference, not independently verified)

Target user: Core users are developers, researchers, and people handling sensitive material who already run local models but are constrained by memory and generation speed. Before bed, they still have long documents to synthesize, codebases to read through, or offline research to organize. Their machine is about to sit idle, so the cost of waiting is lowest. They do not need an instant reply; they need a verifiable, resumable deliverable the next day.

Minimal entry point: Start with a single-machine desktop app that accepts only document analysis and code reading. Split jobs into fixed input chunks, and use SQLite to store manifests, summaries, citations, and outputs. Build an adapter layer for Ollama, local OpenAI-compatible APIs, and WASTE first. WASTE already offers an embeddable C interface and session-saving capability. llama.cpp’s slot interface can save and restore prompt caches. The application must still persist semantic progress itself rather than relying on the cache alone. The first release estimates duration only from recently observed speeds and pauses when the user is active or battery is low. If temperature cannot be read reliably, thermal control falls back to a user-selected power tier.

The strongest case against: Chunking can lose relationships across sections, leaving final conclusions inconsistent. Re-reading material to restore context can consume an already limited overnight window. Duration estimates depend on the model, context, disk, and temperature, so the first job is difficult to estimate accurately. If the system promises morning completion but repeatedly leaves partial work, users will quickly lose trust. Saving a prompt cache is not enough: the application must independently track file versions and citation locations. If files change overnight, old checkpoints may no longer be reusable. Cross-platform differences in sleep, battery, and thermal control also expand the test surface. The condition for continuing is to first make the recovery path reliable for single-machine, read-only, chunkable tasks.

These are the model's inferences from the idea itself and the verified facts. Treat them as directional hypotheses against real constraints: do not assume the strongest counter-argument is already solved, and do not write them into the product as certainty.

## Punching above weight (model inference)

Reach initial users through discussions in LocalLLaMA, model-quantization projects, and low-memory inference guides. Publish a reproducible overnight code-review example showing the same job resuming before and after a power loss. Provide ready-made Ollama and llama.cpp configurations so existing local-model users need to change less of their setup. Lead distribution with the morning result package and failure recovery, not model benchmarks.

## Competitors & gaps (model inference)

- Daymon: Daymon already offers scheduled tasks, background execution, persistent memory, and continuity across runs. Results can also return automatically to a Claude conversation. It directly occupies the “set it up before bed, get results in the morning” use case. Its public site shows Claude Desktop and Claude Code as execution entry points. It addresses background automation for subscription-based assistants, not resource orchestration for slow local inference. The site does not describe completion-time estimates, thermal throttling, or free-disk safeguards, nor does it show chunk-level citations or a list of unfinished work. The opening is to serve extremely slow local-model jobs and turn machine constraints into a delivery commitment. Before a run starts, users must know whether it can meet the deadline—not merely be scheduled.
- Ollama, llama.cpp, and custom scripts: Ollama already provides a local API, request queuing, and controls for keeping models resident. When memory is insufficient, new requests wait and are handled in order. llama.cpp provides slot monitoring and can save and restore prompt caches from files. Skilled users can assemble an overnight pipeline from scripts, schedulers, and databases. These existing capabilities are inference-service components, not a complete state model for long-running jobs. A prompt cache cannot replace records of files read, citation locations, and chunk summaries. Users must still handle deadline estimates, sleep recovery, and morning result packaging themselves. The opportunity is to bring these parts together in a task queue for people who are not operations specialists. The real distinction is not simply queuing, but being able to explain what was completed after a failure.

## How it makes money (model inference)

Sell the desktop scheduler as a one-time per-device license. The base version manages a single-machine queue and result packages; the premium version adds multi-device scheduling, historical archives, and team templates. Users supply their own models, compute, and storage, avoiding per-token charges.

## Source context

Theme: Running Kimi K3 on 29 GB of RAM
Trigger Hacker News post (original English): Run Kimi K3 using 29 GB of RAM at 0.50 tok/s
Heat at capture: ~136 points, 57 comments (point-in-time values)

Points and comments are a historical snapshot from the moment of capture and drift over time. They only explain “why now”; do not present them as precise market numbers.

## Sources

- WASTE — Weight-Aware Streaming Tensor Engine (https://github.com/sqliteai/waste)
- llama.cpp Server README (https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md)
- Ollama FAQ (https://docs.ollama.com/faq)
- Daymon — Run your favorite AI while you sleep (https://daymon.io/)

## Deliverables

- Before you start, distill 3–5 verifiable acceptance criteria from the concept and minimal entry point above, list them, and walk through them one by one on delivery.
- Ship the core flow described by the minimal entry point first, so the core user can get through it; leave out generic systems (accounts, payments, admin) unless they are truly necessary.
- Do not show unverified market numbers in the UI or API.
- Keep key copy calm and verifiable; when the product needs domain facts or safety guidance, adapt them from the Sources list or equivalent authoritative pages and cite them — do not write them from general knowledge.
- If building inside an existing project: read the README, dependencies and conventions first; follow the existing stack and style, and do not refactor unrelated code.
- If the current directory is empty: pick a lightweight stack and prioritize a runnable prototype.
- When done, explain what changed, how to run it, and how to verify it.
- Ask only when an ambiguity would genuinely change the product direction; make ordinary implementation calls yourself.