Qwen3.8-Flash-Next runs at 44 tok/s (43.8 as recorded) on a 128 GB M5 Max, a community run reported by huggingface.co (MLX, 4-bit). It needs about 120 GB of unified memory. It is Alibaba's 125B mixture-of-experts model (6B active), released under the Qwen Custom licence.
Want to run Qwen3.8-Flash-Next faster — or step up to bigger models? Find your Mac →This is a community run: a real run, reported by its source and linked below. The index records it as given and never adjusts it.
| Metric | Value |
|---|---|
| Tokens per second | community44 tok/s |
| Provenance | community run · huggingface.co |
| Quantization | 4-bit |
| Engine | MLX |
| Figure dated | 2026-08-29 |
| Minimum RAM | 120 GB |
| Parameters | 125B MoE (6B active) |
| Chip bandwidth | 614 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 (120 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 |
|---|---|---|---|---|---|---|
| community47 tok/s | 4-bit | MLX | 128 GB | coding task (prompt length not stated) | mtplx.com | MTPLX 2.10.0 mtplx serve, plain autoregressive (MTP off); flat 4-bit g64 'Bare Speed' pack. Published by the runtime's author. |
| community43.8 tok/s | 4-bit | MLX | 128 GB | — | huggingface.co | MTPLX 2.10.0, plain autoregressive (MTP off); 'Optimized Speed' pack: 4-bit g64 with 8-bit sparse-attention projections. |
| community28.5 tok/s | oQ4e | MLX | 128 GB | 1k | omlx.ai | 40-core GPU. oMLX v0.6.3, MTP off (-mtp checkpoint), thinking on. |
Ollama — the index has not verified an Ollama tag for Qwen3.8-Flash-Next, 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: Search the Ollama library → · model card →
The 44 tok/s figure above came from MLX at 4-bit; other runtimes and quants run at different speeds.
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
Alibaba's preview of the Qwen4 architecture (2026-08-26): 125B MoE with 6B active plus a 51B n-gram embedding, Gated DeltaNet + sparse attention, native image and video input, 262K context. SWE-Bench Pro 62.5 (vendor scaffold), LiveCodeBench v6 91.9, GPQA 91.7. 4-bit MLX is 112 GB, a 128 GB-tier model; plain-decode community runs measured 43.8–47 tok/s on an M5 Max (MTPLX 4-bit) and 57 on an M5 Ultra (llama.cpp IQ4_NL). Qwen Community 1.0 license: local use is unrestricted, but coding/office 'AI Work Assistant' products and model-as-a-service need a separate commercial license.
Qwen Custom licence · 125B MoE · 262K context · LLMCheck capability 47/50 · see its leaderboard rank →
Qwen3.8-Flash-Next needs about 120 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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