The LLMCheck index estimates Qwen3.8-27B at ~26 tok/s on an M4 Max at Q4_K_M, a figure from its published bandwidth model, not a measured run. It needs about 19 GB of unified memory. It is Alibaba's 27.8B dense model, released under the Apache 2.0 licence.
Want to run Qwen3.8-27B 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 | MLX |
| Figure dated | 2026-09 |
| Minimum RAM | 19 GB |
| Parameters | 27.8B |
| Chip bandwidth | 546 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 (19 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 | MLX | 64 GB | — | LLMCheck estimator | — |
Ollama — install it (brew install ollama or the app from ollama.com), then pull the model by its tag, checked on ollama.com on 2026-10-17:
MLX — pick a build from Hugging Face’s MLX listings, then run it with its repo id:
Downloads: Search the Ollama library → · model card →
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
Alibaba's Apache 2.0 27.8B dense vision-language model (2026-08-14): image and video input, 262K context (1M with YaRN), thinking mode with reasoning_effort. Runs 4-bit in ~19 GB, so it fits a 24 GB Mac; ~29 tok/s estimated on M5 Max, and BGR's review measured ~55 on an M5 Ultra and ~40 on an M3 Ultra — within 4% of the index's estimates. Model card: SWE-Bench Pro 61.7 (vendor scaffold), Terminal-Bench 2.1 73.0, LiveCodeBench v6 90.3, GPQA 89.2. 1,000+ GGUF quants; Ollama, llama.cpp and LM Studio supported.
Apache 2.0 licence · 27.8B · 262K context · LLMCheck capability 46/50 · see its leaderboard rank →
Qwen3.8-27B needs about 19 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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