The job each one does
The cleanest way to think about it: Ollama is a player. kolm is a producer. Ollama runs the model someone else trained. kolm makes the model that runs.
You can run a kolm-compiled artifact on top of an Ollama-managed runtime; the two layers don't fight. The tension is only when someone says "I'll just use Ollama" - that means "I'll use a generic open-weight model that wasn't trained on my task." For chat, that's fine. For a task that needs to behave a specific way (your support tickets, your code review style, your medical triage rules), you need the compile step.
Side-by-side
| Ollama | kolm | |
|---|---|---|
| What it is | Runtime for open-weight models | Compiler that produces task-specific artifacts |
| Output | A running model server (localhost:11434) |
A signed .kolm file (≤3 GB) |
| Training step | none - you use the weights as-shipped | yes - distillation + LoRA from your data + frontier teacher |
| Quality gate | no - generic benchmarks only | K-score on a held-out test set, gated at 0.70 default |
| Receipts / signing | no | HMAC-SHA256 receipt chain over every output |
| Multimodal recall | bring your own RAG | bundled - sqlite-vec index ships in the artifact |
| Speculative decoding | model-internal only | recipe pack (deterministic drafts) + model speculation |
| Portability | runtime per OS, weights per machine | one artifact, byte-exact, runs on phone/laptop/server/edge |
| Audit trail | none | cryptographic chain from manifest -> output |
| License | MIT runtime | RS-1 spec MIT, SDK MIT, paid managed compile |
When to use Ollama
Use Ollama when you want a generic chat model running locally and you don't care that it's untrained on your specific data.
# works great for: "I want a local LLM I can chat with."
ollama pull llama3
ollama run llama3
When to use kolm
Use kolm when you have a specific task, a small set of examples, and you want the resulting model to behave like the frontier on that task - while running locally with proof.
# classify support tickets the way your team does: kolm compile "classify support ticket urgency" \ --examples ./tickets.jsonl \ --data ./policy/ \ --base qwen2.5-7b ok wrote support-triage.kolm k_score=0.91 signature=hmac-sha256 kolm run support-triage.kolm "customer can't reset password" --receipt
Can I use both?
Yes. kolm run ships with an embedded llama.cpp; you don't need Ollama to run a .kolm. But if you already operate Ollama as your inference layer, the runtime adapter is straightforward - the artifact contains a base-model pointer that any GGUF-compatible runtime understands.
Verdict
If your question is "how do I run a model locally?" use Ollama. It is the right tool for that question.
If your question is "how do I make the model behave a specific way on my data, run it locally, and prove what it said?" use kolm. The compile step is the difference.
Adjacent comparisons: vs RAG - vs fine-tuning - full comparison table