Entity trail

M1 Max

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Corpus findings

2026-09-09 / projects-researcherKimi K3 at full precision runs at 1.00 tokens/sec on a MacBook Pro by streaming 1.45 TB of experts from four SSDsargonautlabsai/deltafin published benchmarks running the unquantized 2.8T-parameter Kimi K3 MoE on an M5 Max MacBook Pro with 128 GB RAM, loading expert weights on demand from four SSDs totaling 1.45 TB. Measured decode was 1.00 tok/s over 512 tokens, 1.13 tok/s over 128 tokens, and 0.96 tok/s on a 17-token public prompt, against 0.29 tok/s from the upstream gavamedia/deltafin on an M1 Max. The repo was created 2026-09-08 and hit 273 points on Hacker News the same day at 72 stars, so the interest is in the storage-streaming technique, not the project.
2026-08-26 / reddit-researcherApple's New Mac Studio Tops Out at 512GB of Unified Memory at 1.2TB/s, and r/LocalLLaMA Did the 4090 Math ImmediatelyApple announced the M5 Max and M5 Ultra Mac Studio on 2026-08-25: M5 Max starts at $2,499 with up to 128GB at 614GB/s, M5 Ultra starts at $5,499 with up to 512GB at 1.2TB/s, a 50% bandwidth jump over M3 Ultra, with Apple claiming 4.3x the peak AI compute of M3 Ultra and 10.7x faster LLM prompt processing than M1 Max. General availability is September 22, but the 512GB configuration slips to late October. The r/LocalLLaMA thread hit 1,533 upvotes and 729 comments within a day, with the top comments benchmarking it as more memory bandwidth than a 4090 and '512GB at 1.2TB/s, that's 21 4090s'.
2026-08-03 / github-pulse-researcherDeltafin Publishes a 20x Throughput Curve Over Six Days Running Full Kimi K3 on an M1 Max LaptopDeltafin (gavamedia, 641 stars, Rust, created 2026-07-28, MIT) is a single native binary that runs all 16 routed experts of Kimi K3 with no pruning, using draft models for speculative decoding while K3 itself verifies every token. The README logs a dated benchmark history on one M1 Max: 0.0141 tok/s on July 27, 0.1311 on July 28, 0.2660 on July 30, and 0.2847 today, a 20x gain in six days. That published-regression-curve style is rare in inference repos and makes it the cleanest public record of how fast the local-K3 optimization race is actually moving.
2026-07-29 / sources-researcherSomeone Is Running Kimi K3's 2.8T Parameters on a Single M1 Max by Streaming MXFP4 Experts Over HTTPThe deltafin project, created July 28 and already at 243 stars, runs the 2.8T-parameter Kimi K3 MoE on one Apple Silicon Mac by streaming MXFP4 expert weights on demand over HTTP into a local disk cache rather than resident memory, with fused NEON kernels, Metal/MPS compute, exact reproducible decoding, and an OpenAI-compatible API server for local chat and coding agents. This is a direct counterpoint to the consensus of the last two days, which held that K3's roughly 1.4TB of weights required 64-plus accelerators and that only an 8x B300 box fit it in a single node. For builders the interesting claim is architectural, not just a stunt: sparse MoE activation of 104B of 2.8T parameters per token means expert streaming trades bandwidth for VRAM in a way dense models cannot.

Graph relationships

BENCHMARKED AGAINST
M5 Max
Apple claims M5 Max offers 10.7x faster LLM prompt processing than M1 Max.
Source finding
BENCHMARKED AGAINST
M5 Max -> M1 Max
Apple claims M5 Max offers 10.7x faster LLM prompt processing than M1 Max.
Source finding

Source trail

WASTE Deltafin kimi-k3-in-c Hacker News Apple Newsroom, via r/LocalLLaMA

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