Home / Models / Ling-3.0-tiny

How to Run Ling-3.0-tiny Locally — Setup Guide (September 2026)

Ling-3.0-tiny is a 7.9B Mixture-of-Experts model with only ~1.3B parameters active per token — the same architecture family as the much larger Ling-3.0-flash, shrunk to something that fits an 8 GB laptop GPU entirely in VRAM. Being MoE at this size is unusual, and it is the point: roughly the knowledge of an 8B model at roughly the generation speed of a 1B one.

Numbers you can trust

We don't print guessed speeds. Every measured number on this page came from TurboLLM's own auto-tuner running Ling-3.0-tiny on real hardware — labeled as such. When you run it yourself, TurboLLM benchmarks it on your exact GPU and shows a VRAM-fit verdict before you load, and the real measured tokens/sec once it's running.

Does it fit your GPU?

7.9B total parameters across 128 experts (8 active, ~1.3B active per token), MIT-licensed, 24 layers of which only 6 keep a growing KV cache. Native 131K context.

Ling-3.0-tiny
inclusionAI (Ant Group) · GGUF
MoE · ~1.3B active · 128 experts
toolsthinkingcode
VRAMFit
8 GBQ4_K_M (4.9 GB) or Q5_K_M (5.7 GB) fully in VRAM with room for context · 131K context
12 GB+Q8_0 (8.4 GB) fully in VRAM — near-lossless, still fast · 131K context
bartowski/Ling-3.0-tiny-GGUF
View on Hugging Face →

How to run it

  1. Install TurboLLM

    npx turbollm — no install step, works on Windows, macOS, and Linux. It detects your GPU and auto-provisions a matching llama-server build (no CUDA toolkit, no Python, no compiler).

  2. Get the model

    Paste bartowski/Ling-3.0-tiny-GGUF into TurboLLM's in-app Hugging Face search — it lists every quant with a VRAM-fit verdict against your real free VRAM, and downloads are resumable and SHA-256 verified.

  3. Let auto-fit pick the setup

    Flip the auto-fit toggle and TurboLLM chooses the GPU/CPU layer split (and, for a Mixture-of-Experts model, the expert-offload split) for your card — the same manual flag-hunting every other guide walks you through by hand.

  4. Load it

    TurboLLM benchmarks the model on your exact GPU as it loads and shows the real measured tokens/sec — not a number copied from someone else's card.

  5. Use it

    Chat in the built-in UI, or point any OpenAI- or Anthropic-compatible tool (including Claude Code) at TurboLLM's local API — see the API docs.

FAQ

Will Ling-3.0-tiny run on my GPU?

Check the VRAM table above for the tier closest to your card. If you're not sure which tier you're in, or want picks tailored to your exact hardware, use What can I run? — enter your VRAM (or Apple unified memory) and it suggests the model, quant, and offload split that fits, the same logic TurboLLM's auto-fit runs when you load a model.

Which quant should I use for Ling-3.0-tiny?

Pick the highest quant that fits your VRAM with room left over for the KV cache and compute buffer — not just the model weights. As a starting point, Q4_K_M is usually the sweet spot (small quality loss for a large size drop); step up to Q5_K_M/Q6_K/Q8_0 if you have headroom, or down to Q3_K_M/an IQ quant if you don't. TurboLLM shows a VRAM-fit verdict for every quant against your card's real free VRAM before you download — see Quantization explained for what the letters and numbers actually mean, or llama.cpp flags that actually matter for the other settings worth knowing.

How is running Ling-3.0-tiny on TurboLLM different from Ollama or LM Studio?

All three can run Ling-3.0-tiny as a GGUF. The differences show up around the edges: TurboLLM auto-tunes the quant and GPU/CPU split against your card's measured free VRAM rather than a fixed default, shows real generation speed instead of a guess, and lets you add a community fork — like the one behind this page, where relevant — without compiling anything yourself. Ollama and LM Studio each ship one blessed runtime you can't swap out. If your current setup already works well for you, there's no need to switch.

Run it now

$ npx turbollm

One command detects your GPU, provisions the right engine, and opens the UI. New here? Start with Install & first run and Quantization explained. On different hardware, see RTX 4060, RTX 3060 or RTX 5060 Ti. Also see how to run it or how to run it.