Releases·Tool Release
Qwen3.8-27B at 144 tok/s on an M5 Max MacBook Pro
Inco released Splash, an open-source inference engine built for Apple silicon, running Qwen3.8-27B at up to 144 tok/s on an M5 Max MacBook Pro. It claims up to 3× Ollama's decode speed and 2× oMLX, with nearly 4× gains when agents fan out into sub-agents. It ships as a CLI server plus an LM Studio runtime, targeting local agent work.
316 engagement2 sourcesSat, Sep 19, 2026, 02:11 AM
AI Overview
- Inco Splash, an open-source inference engine for Apple silicon, claims up to 3× Ollama and 2× oMLX decode speed, ~4× on agent fan-out
- Splash requires M3 or newer, macOS 26.4+, and 36 GB; installs via brew and serves incoai/Qwen3.8-27B-Splash with one command
- Splash integrates with Claude Code, OpenCode, Codex, and Hermes, and ships as a runtime inside LM Studio Bionic
Related
Releases·Model Release
SiliconSpecies ships Swift-Qwen3.8-27B 4-bit package for Mac-only Splash engine, hitting 87.8 tok/s at 64k context on M5 Max
A community-built Hugging Face package, SiliconSpecies/Swift-Qwen3.8-27B-Splash, brings a 4-bit Swift Qwen 27B model to the Mac-only Splash inference engine. Splash documents its package format but ships no converter, so the byte-level layout was reverse-engineered from engine source and verified against a reference package. Reported decode speeds on an M5 Max reach 87.8 tok/s at 64k context, versus 36.6 tok/s for the same weights in oMLX.
A community-built Hugging Face package, SiliconSpecies/Swift-Qwen3.8-27B-Splash, brings a 4-bit Swift Qwen 27B model to the Mac-only Splash inference engine. Splash documents its package format but ships no converter, so the byte-level layout was reverse-engineered from engine source and verified against a reference package. Reported decode speeds on an M5 Max reach 87.8 tok/s at 64k context, versus 36.6 tok/s for the same weights in oMLX.
23 engagement1 sourceredditTue, Sep 22, 2026, 10:33 AM
Social·Community
Splash on a 40-core M5 Max: +20% decode by tuning the kernels for your own chip
A developer found Splash's default kernel rules were measured only on 16/20-core M5s and a 32-core M4 Max, so a 40-core M5 Max runs suboptimal guesses. Using Splash's built-in tune-kernels developer tool to benchmark every quantized matrix-multiply layout on their machine yielded roughly 20% faster decode, with split-K layouts winning for the 8-row draft-token step.
A developer found Splash's default kernel rules were measured only on 16/20-core M5s and a 32-core M4 Max, so a 40-core M5 Max runs suboptimal guesses. Using Splash's built-in tune-kernels developer tool to benchmark every quantized matrix-multiply layout on their machine yielded roughly 20% faster decode, with split-K layouts winning for the 8-row draft-token step.
24 engagement1 sourceredditFri, Sep 25, 2026, 01:13 PM
Releases·Tool Release
mlx-dspark v0.10.0 speeds Qwen3.8-27B up to 3× on Apple Silicon
mlx-dspark, an MLX port of DeepSeek's DSpark speculative-decoding drafters, now supports Qwen3.8-27B via RadixArk's drafter, achieving up to ~3× speedups on Apple Silicon. Benchmarks on an M4 Pro show 2.45× mean speedup for 8-bit targets, with code runs hitting 3.18×.
mlx-dspark, an MLX port of DeepSeek's DSpark speculative-decoding drafters, now supports Qwen3.8-27B via RadixArk's drafter, achieving up to ~3× speedups on Apple Silicon. Benchmarks on an M4 Pro show 2.45× mean speedup for 8-bit targets, with code runs hitting 3.18×.
210 engagement1 sourceredditFri, Aug 14, 2026, 09:54 PM
Social·Community
oMLX vs Rapid-MLX vs Splash vs MTPLX on M3 Max 36 GB: 110 tok/s on Qwen3.6-35B-A3B, ~32 tok/s on Qwen3.8-27B
A Reddit user tested four Apple Silicon local inference engines on a new 14" M3 Max MacBook Pro (36 GB) with Qwen3.8-27B-4bit and Qwen3.6-35B-A3B-4bit. Rapid-MLX was fastest on the MoE model (~110-118 tok/s) while Splash edged the dense model (~32 tok/s). The 125B Qwen3.8-Flash-Next was deemed impractical on 36 GB, needing 96 GB+.
A Reddit user tested four Apple Silicon local inference engines on a new 14" M3 Max MacBook Pro (36 GB) with Qwen3.8-27B-4bit and Qwen3.6-35B-A3B-4bit. Rapid-MLX was fastest on the MoE model (~110-118 tok/s) while Splash edged the dense model (~32 tok/s). The 125B Qwen3.8-Flash-Next was deemed impractical on 36 GB, needing 96 GB+.
18 engagement1 sourceredditMon, Oct 5, 2026, 03:11 PM
Social·Community
User benchmarks Qwen3.8-27B thinking modes on Apple M5 Max MacBook Pro
A user benchmarked Qwen3.8-27B on a MacBook Pro M5 Max using oMLX, comparing thinking xhigh against thinking off. Thinking off sharply reduces output quality, dropping the model behind Qwen3.6-35B-A3B and other faster MoE models. The result matters for practitioners choosing local inference configurations on Apple silicon.
A user benchmarked Qwen3.8-27B on a MacBook Pro M5 Max using oMLX, comparing thinking xhigh against thinking off. Thinking off sharply reduces output quality, dropping the model behind Qwen3.6-35B-A3B and other faster MoE models. The result matters for practitioners choosing local inference configurations on Apple silicon.
36 engagement1 sourceredditSat, Aug 29, 2026, 07:17 PM