Does it fit your GPU?
127.5B total parameters across 512 experts (8 active per token, ~5.1B active), MIT-licensed, 42 layers of which only 6 keep a growing KV cache — so even a large context window stays cheap. The GGUF reports a 131K native context.
| VRAM | Fit |
|---|---|
| 16 GB + 64 GB RAM | AD-IQ2_XXS (39.2 GB) with most experts offloaded to system RAM — the configuration measured below · 32K context |
| 24 GB + 64 GB RAM | AD-IQ2_M (49.1 GB) or AD-IQ3_XXS (57.1 GB) with expert offload · 131K context |
| 48 GB + 128 GB RAM | AD-IQ4_XS (74.9 GB) or AD-Q4_K_M (79.3 GB) with expert offload · 131K context |
AtomicChat/Ling-3.0-flash-GGUFHow to run it
Install TurboLLM
npx turbollm— no install step, works on Windows, macOS, and Linux. It detects your GPU and auto-provisions a matchingllama-serverbuild (no CUDA toolkit, no Python, no compiler).Get the model
Paste
AtomicChat/Ling-3.0-flash-GGUFinto 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.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.
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.
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.
feat/bailingmoe3) fork instead?The bailingmoe3 architecture is not in mainline llama.cpp, so stock builds refuse the file outright. Paste https://github.com/AtomicBot-ai/atomic-llama-cpp-turboquant into TurboLLM's "Add via git repo" flow, fill in the branch field with feat/bailingmoe3 (it is not the repo's default branch), and it compiles with CUDA in one pass — no cmake, no toolchain hunting. One setting matters as much as the build: the GGUF declares an MTP layer it does not actually ship, so speculative decoding must be set to off in the model's advanced config, or every load fails in under a second with "context type MTP requested but model doesn't contain MTP layers".
Measured, not guessed
| RTX 5070 Ti 16 GB · 64 GB system RAM | TurboLLM (measured) |
|---|---|
AD-IQ2_XXS, 34 of 42 expert layers on CPU (auto-tune's pick) · 32K ctx, f16 KV, flash attention on | 36.4 t/s in chat (32.2 t/s raw API) · 12.3 GB VRAM |
| Same quant, 30 expert layers on CPU — fastest found, only ~0.5 GB VRAM headroom left | 34.7 t/s · 15.5 GB VRAM |
| Same quant, all 42 expert layers on CPU — the safe setting for a busy desktop | 28.0 t/s · 5.1 GB VRAM |
| Prefill (prompt processing) at the recommended setting | 303 t/s |
Measured in TurboLLM on an RTX 5070 Ti 16 GB with 64 GB of system RAM, August 2026 — swept by TurboLLM's own auto-tuner, read from the running llama-server's own metrics, with the in-chat figure taken from the app's footer.
This one needs system RAM, not just VRAM. At AD-IQ2_XXS the offloaded experts want roughly 40 GB of free system RAM on top of what is on the GPU — a 16 GB-RAM machine cannot run it, however big its graphics card is. Speed also does not scale smoothly as you pull experts back onto the GPU: on the measured box, moving from 30 to 20 CPU expert layers used the same VRAM but dropped generation from 34.7 t/s to 5.7 t/s. Let auto-tune find the point rather than interpolating.
FAQ
Will Ling-3.0-flash 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.
Why does Ling-3.0-flash suddenly get slow or stutter?
The usual cause is silent VRAM spill: once a model's weights plus its KV cache stop fitting in VRAM, the driver quietly offloads the overflow to system RAM over PCIe, and that path runs 5–10× slower with no error message — just a sudden slowdown. TurboLLM's auto-fit sizes the quant and GPU/CPU split against your card's real free VRAM (including KV-cache growth at your chosen context) specifically to avoid this, and shows a VRAM-fit verdict before you load rather than after it's already crawling. Full mechanics in VRAM spill explained.
Which quant should I use for Ling-3.0-flash?
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-flash on TurboLLM different from Ollama or LM Studio?
All three can run Ling-3.0-flash 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
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 5070 Ti, RTX 5080 or RTX 4090. Also see how to run it, how to run it or how to run it.