Qwen3.8-27B runs at 17 tok/s (16.5 as recorded) on a 48 GB M5 Pro, a published figure reported by frandroid.com (llama.cpp, 4-bit, single stream; prompt length not stated). 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 a published figure: 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 | sourced17 tok/s |
| Provenance | published figure · frandroid.com |
| Quantization | 4-bit |
| Engine | llama.cpp |
| Context | single stream; prompt length not stated |
| Figure dated | 2026-10-15 |
| Minimum RAM | 19 GB |
| Parameters | 27.8B |
| Chip bandwidth | 307 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 |
|---|---|---|---|---|---|---|
| community16.81 tok/s | 4-bit | LM Studio | 48 GB | 512-token coding answers, median of 5 at T 0.6 | github.com | MacBook Pro, 20-core GPU. LM Studio MLX runtime 1.13.1, lmstudio-community MLX 4-bit; greedy 16.88; flagged for 511 of 512 completion tokens. Run by the Kapitsa engine's developers with their own harness. |
| sourced16.5 tok/s | 4-bit | llama.cpp | 48 GB | single stream; prompt length not stated | frandroid.com | Frandroid's RTX Spark review: MacBook Pro 14 M5 Pro (18-core CPU, 48 GB), llama.cpp b11582 on Metal, a 16.5 GB 4-bit GGUF whose type the review does not name. |
| community16.33 tok/s | 4-bit | MLX | 48 GB | 512-token coding answers, median of 5 at T 0.6 | github.com | MacBook Pro, 20-core GPU. mlx-lm 0.32.2 (MLX 0.32.4), no MTP path; greedy 16.92. Run by the Kapitsa engine's developers with their own harness. |
Ollama — install it (brew install ollama or the app from ollama.com), then pull the model by its tag, checked on ollama.com on 2026-10-24:
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 17 tok/s figure above came from llama.cpp 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.
| Chip | Speed | Quant | Engine | Bandwidth |
|---|---|---|---|---|
| M5 Ultra | sourced48 tok/s | oQ4e | MLX | 1228 GB/s |
| M3 Ultra | community38 tok/s | 4-bit | MLX | 819 GB/s |
| M5 Max | estimated29 tok/s | Q4_K_M | MLX | 614 GB/s |
| M4 Max | estimated26 tok/s | Q4_K_M | MLX | 546 GB/s |
| M3 Max | estimated19 tok/s | Q4_K_M | MLX | 400 GB/s |
| M4 Pro | estimated13 tok/s | Q4_K_M | Ollama | 273 GB/s |
| M6 | community9 tok/s | QAT | LM Studio | 170 GB/s |
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:
As an Amazon Associate, LLMCheck earns from qualifying purchases. These affiliate links cost you nothing extra and help keep the LLMCheck index free.
See how Qwen3.8-27B stacks up against other models on your specific Mac hardware.
Open Compare Tool Full Leaderboard