# AI use cases for Kimi K3 in C (FareedKhan-dev/kimi-k3-in-c)

Repo: [FareedKhan-dev/kimi-k3-in-c](https://github.com/FareedKhan-dev/kimi-k3-in-c) | Use cases: 4 | Page: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/

A C99 inference engine that runs the 2.78-trillion-parameter Kimi K3 model on a single CPU with no GPU, streaming weights from disk when RAM is small.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Run Kimi K3 text completions on an 8 GB laptop: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/run-kimi-k3-batch-completion-on-8gb-laptop/
- Chat with Kimi K3 offline and save the transcript: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/chat-with-kimi-k3-offline-and-keep-history/
- Check that output stays identical across memory presets: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/verify-identical-output-across-memory-presets/
- Export routed-expert cache traces for analysis: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/export-expert-cache-traces-for-analysis/

## How to run Kimi K3 text completions on an 8 GB laptop with Kimi K3 in C

Repo: [FareedKhan-dev/kimi-k3-in-c](https://github.com/FareedKhan-dev/kimi-k3-in-c) | Difficulty: Advanced | Time: 1 h 0 min (excludes the checkpoint download) | Basis: From the README

Generate a short continuation with the full 2.78T model on one CPU, streaming weights from disk.

**Who it is for:** A developer or researcher with a laptop of about 8 GB of RAM who wants to see the largest open model run locally, with no GPU.

**The problem:** Frontier-scale models normally need GPU clusters, so most people cannot run them at all.

**The outcome:** The model produces a correct short continuation, and the run reports peak memory near 8.24 GB, at a slow pace of about 30 s per token.

### Steps

1. Build the engine from the repo with a C99 compiler. (inferred, check the README)
2. Download the checkpoint with download-model.sh, with HF_TOKEN set in the environment. (inferred, check the README)
3. ./bin/k3 --list-presets
4. ./bin/k3 ~/k3model --trunk ~/k3trunk --preset laptop --tok ~/k3model --prompt "The capital of France is" --gen 8 --incremental

### Prompt to paste into your agent

```text
In the repo FareedKhan-dev/kimi-k3-in-c, run the laptop preset with --trunk <trunk_dir> and checkpoint directory <checkpoint_dir>, using the prompt "The capital of France is" and --gen 8 --incremental. Expect generated text and a PEAK RSS line. Verify that the reported peak RSS stays near 8.24 GB.
```

### Caveat

The README calls the 8 GB path a proof of life, not an interactive preset, so expect roughly 26 to 33 seconds per token on a laptop.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/run-kimi-k3-batch-completion-on-8gb-laptop/

## How to chat with Kimi K3 offline and save the transcript with Kimi K3 in C

Repo: [FareedKhan-dev/kimi-k3-in-c](https://github.com/FareedKhan-dev/kimi-k3-in-c) | Difficulty: Intermediate | Time: 1 h 0 min (excludes the checkpoint download) | Basis: From the README

Run multi-turn chat with the official XTML format on your own CPU, saved to a JSONL file.

**Who it is for:** A user who wants a private, offline chat session with Kimi K3 and a local record of the conversation.

**The problem:** Hosted chat services send every message to a third party, and a model this large is hard to run at home.

**The outcome:** A REPL session where each turn is answered by Kimi K3, with the full record kept in my-session.jsonl for later use.

### Steps

1. Build the engine and download the checkpoint and trunk as described in the batch use case. (inferred, check the README)
2. ./bin/k3 ~/k3model --trunk ~/k3trunk --preset desktop --tok ~/k3model --chat --system "You are a helpful assistant." --history my-session.jsonl --incremental
3. Type a message, then use /reset to start over or /exit to quit.
4. Add --no-think to the same command when you want answers without the thinking block.

### Prompt to paste into your agent

```text
In the repo FareedKhan-dev/kimi-k3-in-c, start the --chat REPL with --history <history_file> and --incremental. Send one user message and expect an assistant reply. Verify that the history file contains a user record and an assistant record in JSONL form.
```

### Caveat

The history file can contain every user message and the full assistant reasoning record, so store it as sensitive data.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/chat-with-kimi-k3-offline-and-keep-history/

## How to check that output stays identical across memory presets with Kimi K3 in C

Repo: [FareedKhan-dev/kimi-k3-in-c](https://github.com/FareedKhan-dev/kimi-k3-in-c) | Difficulty: Advanced | Time: 1 h 15 min (excludes the checkpoint download) | Basis: From the README

Run the same prompt on a small and a large preset and compare the generated text.

**Who it is for:** A researcher or tester who needs evidence that a model gives the same answer regardless of how much memory the machine has.

**The problem:** Changing memory settings can silently change results, which makes benchmarks and bug reports hard to trust.

**The outcome:** Two runs with different presets print the same generated text, and only the timing lines differ.

### Steps

1. Run the laptop preset command with --ids so no tokenizer is needed: ./bin/k3 ~/k3model --ids 1008,10484,318,15383,387 --trunk ~/k3trunk --preset laptop --gen 8 --incremental (inferred, check the README)
2. ./bin/k3 ~/k3model --trunk ~/k3trunk --preset server --tok ~/k3model --prompt "def fibonacci(n):" --gen 28 --incremental
3. Save the generated text from each run.
4. Diff the two outputs and confirm they match.

### Prompt to paste into your agent

```text
In the repo FareedKhan-dev/kimi-k3-in-c, run the same prompt with the laptop preset and the server preset, using --incremental. Expect two generated text blocks with timing lines. Verify that the generated text is byte-identical between the two runs.
```

### Caveat

The README reports byte-identical output across machine sizes, but the server run needs a machine with far more memory and is only a timing comparison in practice.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/verify-identical-output-across-memory-presets/

## How to export routed-expert cache traces for analysis with Kimi K3 in C

Repo: [FareedKhan-dev/kimi-k3-in-c](https://github.com/FareedKhan-dev/kimi-k3-in-c) | Difficulty: Advanced | Time: 1 h 30 min (excludes the checkpoint download) | Basis: From the README

Dump expert usage traces from a run so the included cache simulator can replay them.

**Who it is for:** A systems researcher who wants to study how the routed-expert cache behaves under real Kimi K3 routing.

**The problem:** Tuning a cache for a 2.78T mixture model needs real routing data, and guessing from theory is unreliable.

**The outcome:** A directory containing expert_hist.json and expert_trace.bin, ready to replay with tools/sim_cache.py.

### Steps

1. Build the engine and download the checkpoint and trunk as in the batch use case. (inferred, check the README)
2. Run a generation with --dump-cache-trace <trace_dir> added to the laptop preset command. (inferred, check the README)
3. python tools/sim_cache.py <trace_dir> (inferred, check the README)

### Prompt to paste into your agent

```text
In the repo FareedKhan-dev/kimi-k3-in-c, run a short generation with --dump-cache-trace <trace_dir> and --incremental. Expect expert_hist.json and expert_trace.bin in that directory. Verify both files exist and are non-empty before replaying them with tools/sim_cache.py.
```

### Caveat

The README does not show the exact arguments for sim_cache.py, so check its usage in the tools folder before running it.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fareedkhan-dev-kimi-k3-in-c/export-expert-cache-traces-for-analysis/