d1-omni-600M

How to run d1-omni-600M locally starts with a VRAM check, then a runner choice, then one clean load at Q4. This runbook is the install path — the listing covers whether the weights belong on your card.

Check VRAM before you fetch d1-omni-600M

Confirm the VRAM chart on the d1-omni-600M listing first. If Q4 at 4k does not fit your card, pick a smaller sibling instead of fighting a load that spills into system RAM.

Get the files from LiquidAI/d1-omni-600M. Prefer an official or well-known GGUF. Unofficial dumps often ship the wrong chat template.

Choose a runner for d1-omni-600M

Ollama is the shortest path for a local chat UI. Install it, confirm it sees the GPU, then point it at the local blob or a matching library tag.

llama.cpp gives you flags for layers, context, and sampler. Build or install a CUDA, Metal, or Vulkan binary. Run with -m at the GGUF and -c at a context you can hold.

vLLM is for an OpenAI-compatible HTTP API on a GPU server. Use it when you already serve other models that way, not for a first desktop test.

First load of d1-omni-600M that actually fits

Start at Q4_K_M and 4k context. Raise context only after the card stops swapping. Drop quant if shared GPU memory climbs — that is a failed fit even if the process stays alive.

Windows: prefer CUDA or Vulkan and close overlays that grab the GPU. macOS: use Metal. Linux with NVIDIA: confirm nvidia-smi sees the card before blaming the weights.

Lock the d1-omni-600M setup once it answers

Write down quant, context, tokens per second, and the GPU used. Lock a short system prompt and a sampler preset so later tests stay comparable.

Do not expose an unauthenticated local server to the public internet. Bind to localhost or a trusted LAN and add auth if anyone else can reach the port.