← Kimi K2
David Ondrej
Fine-Tune the biggest open-source models (even with a bad PC)
⏱ 29 min video · 3 min read7 Jul 2026Worth watching
TL;DR
David Ondrej demonstrates how to fine-tune Kimi K2, a 1-trillion-parameter open-source model comparable to Claude Opus, using LoRA on Fireworks AI without needing expensive hardware. The full workflow covers dataset selection from Hugging Face, format conversion, uploading to Fireworks AI, and deploying the fine-tuned model publicly.
Key points
1
Kimi K2 is a 1-trillion-parameter open-source model comparable to Claude Opus 4 but 6-8x cheaper, making it a cost-effective fine-tuning target.
2
You do not need expensive hardware: Fireworks AI provides NVIDIA B300 Blackwell GPUs for cloud-based fine-tuning, with a LoRA fine-tune of Kimi K2 costing roughly $38-$131 depending on dataset size.
3
LoRA (Low-Rank Adaptation) freezes base model weights and trains only a small adapter, making trillion-parameter fine-tuning feasible without full retraining costs.
4
Dataset quality is critical: use data from models larger than Kimi K2 (e.g. Fable at ~10-15 trillion parameters) and ensure at least 1,000 rows for effective LoRA fine-tuning.
5
After fine-tuning on Fireworks AI, you can deploy the model as a live endpoint and compare it side-by-side against the base Kimi K2 model via a simple web app.
Actionable insights
→
Go to Fireworks AI, create an account, and use their LoRA fine-tuning feature to fine-tune Kimi K2 without owning any GPUs — the platform handles all compute.
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Source your fine-tuning dataset from Hugging Face and use a dataset generated by a model larger than your target (e.g. Fable datasets) to avoid quality regression.
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Use an AI agent (Pi Agent, Codex, Claude Code) to automate dataset downloading, format conversion to Fireworks-compatible JSONL, and environment setup — do not do this manually.
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Set minimum deployment replicas to 1 on Fireworks AI to prevent spin-down latency if you need always-on inference for your application.
→
For cost efficiency, route planning/complex tasks to a frontier model and routine inference to a fine-tuned open-source model like Kimi K2, which can be 7x cheaper than Claude Opus.
Notable quotes
“Fine-tuned models can outperform different models that are even five times larger in the amount of parameters that they have, purely because they're fine-tuned in a specific domain.”
“A lot of you are wasting money on running everything on Opus or GPT-5 while you could get much better usage if you had the most powerful model for the planning and then the smaller open source models that are faster.”
“If you have a shitty data set you're going to have a shitty model — the data is one of the most important things.”
Worth watching?
✅
Worth watching the full video?
Watch if you want a practical, step-by-step walkthrough of fine-tuning a trillion-parameter model on a budget — the key steps, costs, and tools are all captured here, but the live screen demos and agent interactions add useful context you will miss from this summary alone.
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