Cowboy Coder

[Cowboy Coder @CowboyCoderHQ]

@ddalcu Will put on queue. I’m running extensive benchmarking right now on a parallel mtp improvement that shows promise of increasing code generation by 13-19% and prose benefits of 2-23%.

[Cowboy Coder @CowboyCoderHQ]

@ddalcu Congratulations! Does it run ~double the m5 max for single stream and 4 stream decode as well?

[Cowboy Coder @CowboyCoderHQ]

@u1tra_instinct And concurrent streams?

[Cowboy Coder @CowboyCoderHQ]

@u1tra_instinct How is coding?

[Cowboy Coder @CowboyCoderHQ]

@1337hero Thank you.

[Cowboy Coder @CowboyCoderHQ]

@1337hero Neither have I, if you do end up running the numbers, it will be greatly appreciated.

[Cowboy Coder @CowboyCoderHQ]

@Sergizzzz4 @ddalcu According to my benchmarks so far, yes. I haven’t tested oMLX for about a week, so it may have improved.

[Cowboy Coder @CowboyCoderHQ]

@1337hero Do you happen to have a post on the numbers for the m5 ultra power usage though?

[Cowboy Coder @CowboyCoderHQ]

@1337hero Glm 5.3 flash or qwen 3.8 flash next.

[Cowboy Coder @CowboyCoderHQ]

@volatilemarkts I love tensorfold as much as the next dev, but don’t you think this comparison is a tad unfair. Why not show omlx with mtp and apc on vs tensorfold for a fair engine to engine comparison.

[Cowboy Coder @CowboyCoderHQ]

@NetworkChuck Awesome, I’m doing the same. What printer are you running and how did you wire it to the agent?

[Cowboy Coder @CowboyCoderHQ]

@qadir5000 @NetworkChuck May I recommend the Bambu X2D. Dual nozzle, $650, 20+ sensors, developer mode… the list goes on and on. I had an agent build a mcp for mine. https://t.co/5PzG7LaZtN

[Cowboy Coder @CowboyCoderHQ]

Mark my words, the mlx community is awakening. What holds back local Super Intelligence on Apple silicon isn’t the hardware, it’s the software.

[David Dalcu @ddalcu]

MLX-Serve 26.10.1 is out: speed across the board.

Exactly faster. Qwen3.8 27B with its drafter
+66% on an M5 Ultra,
+28% on an M4 Max,
+37% on an M1 Pro.

Qwen Flash Next:
+11% decode on M4 Max,
+51% prefill on M5 Ultra. (up to ~5400 PP!!!!)

Gemma 4, Qwen, LFM2.5, Spark, Bonsai 2 all faster, output byte-identical to 26.9.6 (across 18 models).

New:
* GGUF models run on our own MLX engine, written in Zig and Metal, instead of the llama.cpp fallback. (experimental)
* 🍣 Sushi-format Flash Next packs run directly.
* Qwen-Image 2.1 edits pictures from instructions.
* Drafting is on by default for every model that supports it.

Huge thanks to everyone who filed, tested and fixed this one: @STRML_, @sbusso, @wutang_superfan , lborloz, LXD-8 @alinselea zeeshanhaque21 @Lojza3D, h9q2cyxvgm-ui @kennethrdegraff, @CowboyCoderHQ , codysk, @Beamsters1, @CerebralCoding_, @AjAbsaki , and more.. THANKS ! Please comment if I did not include your name, for visibility, I know people mostly by their GH handle.

Special thanks to @ashxhart and @Spangler3000 for pushing !

Benchmarked and tested across 5 machines: https://t.co/BDeBKRq6Kd

GH Release Download + Changelog:
https://t.co/Yx9rrU6mcm

Website: https://t.co/qxuPXH75o1

[David Dalcu @ddalcu]

MLX-Serve 26.10.1 is out: speed across the board.

Exactly faster. Qwen3.8 27B with its drafter
+66% on an M5 Ultra,
+28% on an M4 Max,
+37% on an M1 Pro.

Qwen Flash Next:
+11% decode on M4 Max,
+51% prefill on M5 Ultra. (up to ~5400 PP!!!!)

Gemma 4, Qwen, LFM2.5, Spark, Bonsai 2 all faster, output byte-identical to 26.9.6 (across 18 models).

New:
* GGUF models run on our own MLX engine, written in Zig and Metal, instead of the llama.cpp fallback. (experimental)
* 🍣 Sushi-format Flash Next packs run directly.
* Qwen-Image 2.1 edits pictures from instructions.
* Drafting is on by default for every model that supports it.

Huge thanks to everyone who filed, tested and fixed this one: @STRML_, @sbusso, @wutang_superfan , lborloz, LXD-8 @alinselea zeeshanhaque21 @Lojza3D, h9q2cyxvgm-ui @kennethrdegraff, @CowboyCoderHQ , codysk, @Beamsters1, @CerebralCoding_, @AjAbsaki , and more.. THANKS ! Please comment if I did not include your name, for visibility, I know people mostly by their GH handle.

Special thanks to @ashxhart and @Spangler3000 for pushing !

Benchmarked and tested across 5 machines: https://t.co/BDeBKRq6Kd

GH Release Download + Changelog:
https://t.co/Yx9rrU6mcm

Website: https://t.co/qxuPXH75o1

[Cowboy Coder @CowboyCoderHQ]

@ddalcu Excellent progress

[Cowboy Coder @CowboyCoderHQ]

@ddalcu Thanks to everyone. Let’s keep pushing, we’re not finished yet.

[Cowboy Coder @CowboyCoderHQ]

@andyreed Have you considered redirecting all questions to a cross family multi-agent council during your sleeping hours? I implemented it recently and so far experienced excellent results.

[Cowboy Coder @CowboyCoderHQ]

Local ai is the way to go, the main downside in my eyes is that you pay every second you aren’t running max tok/sec, because your hardware is an asset, not just your ticket to intelligence, freedom, and privacy. Who else feels my pain?

[Cowboy Coder @CowboyCoderHQ]

@sudoingX Local ai is the way to go, the main downside in my eyes is that you pay every second you aren’t running max tok/sec, because your hardware is an asset, not just your ticket to intelligence, freedom, and privacy. Who else feels this pain?