Configs › Qwen3.8-27B › Qwen3.8-27B on Mac Max: 41.5 tok/s, 4 measured configs

Qwen3.8-27B on Mac Max: 41.5 tok/s, 4 measured configs

Measured Qwen3.8-27B speeds on Mac Max: fastest 41.5 tok/s single-stream (llama.cpp (speculative decode, mtp)), 4 configs with quant, backend and context, each linked to its source, snapshot 2026-10-29.

Snapshot 2026-10-29 · 413 configs on AI boxes and 288 on GPU cards · 121 models. Every number is generated from the tracker snapshot and linked to where it was measured; nothing here is typed by hand.

Fastest measured single-stream run of Qwen3.8-27B on Mac Max: 41.5 tok/s (llama.cpp (speculative decode, mtp)), snapshot 2026-10-29.

Every measured single-stream run of Qwen3.8-27B on Mac Max

Decode tok/sQuantBackendContextTrustMeasured bySource checkedSource
41.5llama.cpp (speculative decode, mtp)16000Extracted, source-linkedxthomaswang2026-10-19source
31.5MLX-4BITmlxExtracted, source-linkedlizhun7810212026-09-29source
27.4mlx16000Extracted, source-linkedxthomaswang2026-10-19source
26.2vllm/metal16000Extracted, source-linkedxthomaswang2026-10-19source

Backends on Mac Max

Fastest plain-decoding run per backend. Quants can differ between rows.

Qwen3.8-27B on other boxes

Questions and answers

How fast is Qwen3.8-27B on Mac Max?
The fastest of 4 measured configs is 41.5 tok/s single-stream (llama.cpp (speculative decode, mtp)). The slowest is 26.2 tok/s.
Which backend is fastest for Qwen3.8-27B on Mac Max?
mlx at 31.5 tok/s, then vllm/metal at 26.2. Each backend figure is its best plain-decoding row and may use a different quant.
How does Mac Max compare with other boxes for Qwen3.8-27B?
Mac Max best 41.5 tok/s. Other boxes, best measured: Mac Ultra 42.6, Strix Halo 36.8, DGX Spark 23.6. Backends and quants differ, so this is the best known number per box, not a controlled test.

See also: Qwen3.8-27B on every box · Mac Max hardware page (from S$3,499 in Singapore today).

Last updated: snapshot 2026-10-29; newest source check 2026-10-19.

More: all configs · hardware · news · contributors · methodology · llms.txt · llms-full.txt · API (OpenAPI) · JSON snapshot. Built by Altronis, private on-prem AI, Singapore.