Its KDA layers are unverified on Metal, and no Mac measurement has been published. The LLMCheck index estimates Ling-3.1-flash at ~26 tok/s on an M5 Ultra at Q4_K_M, a figure from its published bandwidth model, not a measured run. It needs about 360 GB of unified memory.
Want to run Ling-3.1-flash faster — or step up to bigger models? Find your Mac →This is an LLMCheck index estimate from its published two-term bandwidth model, not a measured run; the index labels it so everywhere it appears. Own this Mac? Submit a real run →
| Metric | Value |
|---|---|
| Tokens per second | estimated~26 tok/s |
| Provenance | estimate · how estimates are made |
| Quantization | Q4_K_M |
| Engine | llama.cpp |
| Figure dated | 2026-10 |
| Minimum RAM | 360 GB |
| Parameters | 560B MoE (25B active) |
| Chip bandwidth | 1228 GB/s |
Each row as the index holds it, with its source. “Mac RAM” is the memory of the Mac a run used, not the model’s requirement (360 GB). The headline figure follows the reference rule: the lower-median measured plain-decode run at 5 bits or fewer (a run whose quant was not stated counts only when no stated-quant run qualifies), else the estimate; a run marked “not used to anchor estimates” is listed here but never becomes the headline.
| Speed | Quant | Engine | Mac RAM | Context | Source | Note |
|---|---|---|---|---|---|---|
| estimated26 tok/s | Q4_K_M | llama.cpp | 512 GB | — | LLMCheck estimator | — |
Ollama — the index has not verified an Ollama tag for Ling-3.1-flash, so it doesn’t print one (a guessed tag fails with “pull model manifest: file does not exist”). Search the Ollama library →
MLX — pick a build from Hugging Face’s MLX listings, then run it with its repo id:
Downloads: Hugging Face repo → · model card →
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
| Chip | Speed | Quant | Engine | Bandwidth |
|---|---|---|---|---|
| M3 Ultra | estimated18 tok/s | Q4_K_M | llama.cpp | 819 GB/s |
Ant Group's MIT-licensed 560B hybrid-linear MoE (13 Oct 2026): 25B active (KDA + MLA layers, small KV cache), 262K context; its SWE-bench Pro 68.7 is on Ant's own LingCode scaffold. Upstream llama.cpp runs it (bailingmoe3), but Q4_K_M is 358 GB, so it needs a 512 GB Mac Studio; a 2-bit 219 GB build might squeeze onto 256 GB with the GPU limit raised, untested. There is no MLX port, its KDA layers are unverified on Metal, and no Mac measurement has been published.
MIT licence · 560B MoE · 262K context · LLMCheck capability 47/50 · see its leaderboard rank →
Ling-3.1-flash needs about 360 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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