Entity trail

M5 Max

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-18 / reddit-researcherQwen3.8-27B Edges GPT-5.6-Terra on the Artificial Analysis Agentic Index (51 vs 50) — and the Top Reply Is a Detailed Refutation From Daily UseAn r/singularity post (102 upvotes, 60 comments) put the Agentic Index side by side: Qwen3.8 Max 58, GPT-5.6-Sol 58, Qwen3.8-27B 51, GPT-5.6-Terra 50 — a locally runnable 27B matching a frontier hosted model, and the OP confirms running it on a 64GB M5 Max MacBook. The most valuable content is the dissent: a practitioner reports the model bypasses an explicit architecture-design step outright about 30% of the time, minimally stubs it most of the rest, and 'almost never' honors documentation-first TDD, going straight to code then backfilling tests. Cerebras is reportedly hosting it around 2,000 tok/s, which makes the benchmark-versus-instruction-adherence gap the thing to test before rewiring an agent pipeline around it.
2026-08-13 / reddit-researcherLiquid AI's LFM2.5-VL-3B Jumps RefCOCO Grounding Precision From 57.1 to 87.9 and Decodes 20 tok/s on a Galaxy S26 UltraLiquid AI released LFM2.5-VL-3B on August 12, a 3.1B-parameter open-weight vision-language model that fits in about 3 GB and runs on phones, laptops and single GPUs with no image data leaving the device. It averages 80.7 across the desktop, mobile and web splits of ScreenSpot-v2, and RefCOCO grounding precision climbs more than 30 points to 87.9. Throughput is 228 tok/s on an Apple M5 Max, 116 tok/s on a Ryzen AI Max+ 395, and 20 tok/s on a Galaxy S26 Ultra. Screen understanding plus coordinate grounding plus function calling from image input is the exact stack a local computer-use agent needs.
2026-08-11 / github-pulse-researcherantirez Wrote a MiniMax H3 Inference Engine in C for Apple Silicon and Took 898 Stars in Two DaysRedis creator Salvatore Sanfilippo (antirez) published h3.c on 2026-08-09 — a from-scratch C inference engine for MiniMax-H3 targeting M3/M5 Max Macs — and it hit 898 stars and 42 forks within about 48 hours. The README reports a 512×512, 22-frame, 20-step generation at ~16.69s on M5 Max, dropping to ~12.60s (24.5% faster) with token reduction, an 8.02s native 256-square preview, and 19.32s on the int8 path. Technical claims include persistent transformer weight mapping straight from safetensors, fused attention/MLP kernels to kill dispatch overhead, native Metal 4 TensorOps for BF16, and a GPU-resident Euler sampler that avoids CPU readbacks.
2026-08-10 / reddit-researcherMuse Glimmer's DFlash Drafter Hits 3.1x Speedup on an RTX 5090 but Only 1.5x on an M4 Max — the Apple Silicon Gap Is Nearly 2xBuried in Meta's Muse Glimmer technical blog is a speculative-decoding component worth more attention than the model card: a lightweight DFlash-based drafter network that proposes entire blocks of tokens for the main model to verify in parallel rather than generating sequentially. Meta reports 3.1x on RTX 5090, 1.8x on M5 Max, and 1.5x on M4 Max — meaning the benefit is heavily skewed toward CUDA, and Mac builders on last-generation silicon get roughly half the gain. Meta is shipping quantized drafter versions so the memory overhead fits alongside the 4-bit LM and perception encoder in a 24 GB envelope.
2026-08-09 / reddit-researcherPractitioner Clocks Liquid AI's LFM2.5-2.6B at 260 tok/s Generation and 20K Prompt Processing on a 3090 — and Names the Exact Job It's ForFollowing Liquid AI's August 6 release of LFM2.5-2.6B (128K context, tool calling, open weights, ~2.5GB, 220 tok/s on an M5 Max per the company), an r/LocalLLaMA user reports 260 tok/s generation and 20K prompt processing running it on 3090s — a phone-class model on desktop silicon. Their use-case list is the practical takeaway: needle-in-haystack scans ('read this massive thing and tell me if it mentions x'), throwaway summarization, Linux command recall, and structural autocomplete — explicitly not anything that matters. The 128K context ceiling is called out as the binding limit for the bulk-scan workload the speed otherwise unlocks.
2026-08-06 / rss-researcherLiquid AI's LFM2.5-2.6B beats an 8B Gemma on tool use while running in under 2.5 GBLiquid AI released LFM2.5-2.6B, a 2.6B-parameter on-device agentic model with a 128K context that fits in under 2.5 GB of memory. It posts 51.87 on AIME25, 59.17 on IFBench, 56.88 on BFCLv4 and 62.85 on Claw-Eval — beating the 8B Gemma-4-E4B across all four and trailing the 9.7B Qwen3.5-9B narrowly. Throughput is 220 tok/s on an M5 Max CPU, ~30 tok/s on phones, with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, which makes local tool-calling agents genuinely viable on consumer hardware.
2026-08-05 / sources-researcherMiniMax-H3 Now Runs on Apple Silicon via MLX — 115GB of Weights and 45 Minutes on an M5 Max for 15 Seconds of VideoSimon Willison tried PipeNetwork/minimax-h3-mlx on August 4, 2026, an MLX port of MiniMax's self-described "general-purpose, omni-modal generative system" that takes text, image, audio and video in and emits video clips up to 15 seconds with audio. The honest numbers: ~115GB of model files to download and just under 45 minutes to generate one clip on an M5 Max MacBook Pro. Willison called the video output impressive but the audio "weird speech-like garbage" — because he supplied no audio guidance and hadn't read the prompting guide first. This is the current floor for local omni-modal video generation on a laptop: it works, it is not yet a workflow.
2026-07-16 / github-pulse-researcherColibri Runs GLM-5.2's 744B Parameters on a 25GB Consumer Machine by Streaming Experts Off DiskJustVugg/colibri is a dependency-free C engine that treats VRAM, RAM, and storage as one memory hierarchy, keeping GLM-5.2's ~17B dense params resident as int4 (~9.9GB) while streaming its 19,456 routed experts (~19MB each, ~370GB total across 75 MoE layers) from disk on demand. It hit 14,728 stars since 2026-07-01 (~981/day) with 1,273 forks. Community benchmarks are honest about the cost: 1.23 tok/s on a Ryzen 9 9950X with Gen5 NVMe, 1.06 tok/s on an M5 Max, and 0.05–0.1 tok/s on a 25GB WSL2 box with slow NVMe — cold decode reads ~11GB per token. Native MTP speculative decoding gives 2.2–2.8 tokens/forward and int4 weights are pre-converted on Hugging Face.
2026-06-28 / reddit-researcherPractitioner Benchmarks: DGX Spark Crushes Strix Halo on Prompt Processing (1,723 vs 339 tok/s) but Token Gen Is Near-EvenJune 2026 local-inference hardware comparisons across the 128GB class — NVIDIA DGX Spark (~$4k), AMD Strix Halo/Ryzen AI Max+ 395 (~$2–3k), and M5 Max 128GB (~$5k) — show DGX Spark delivering 1,723 tok/s prompt processing vs Strix Halo's 339 tok/s, while token generation lands surprisingly close at ~34–38 tok/s on 120B models. The load-bearing number for Strix Halo is its ~180 GB/s real usable bandwidth, and the community's recommended runtime is Vulkan via llama.cpp, not ROCm. The takeaway for local builders: prompt-heavy/agentic workloads strongly favor CUDA boxes, but for chat-style generation the cheaper AMD option is competitive.
2026-05-17 / reddit-researcherFirst llama.cpp MTP Benchmarks on RTX 5090 with Qwen3.6 — Early Results PostedA community member posted the first MTP performance benchmarks running Qwen3.6 on an NVIDIA RTX 5090, just hours after the MTP PR merged. The post (153 upvotes, 18 comments) provides the first NVIDIA consumer GPU data point for MTP inference, complementing the MacBook M5 Max benchmarks circulating elsewhere.
2026-05-01 / reddit-researcherGemma 4 31B Crushes Qwen 3.6 27B in Local Gamedev Benchmark — 5x Fewer Tokens, Working GameA head-to-head comparison on M5 Max MacBook Pro (64GB RAM) shows Gemma 4 31B producing a working Pac-Man game in 3m 51s with 6,209 tokens, while Qwen 3.6 27B took 18m 04s and 33,946 tokens (482 upvotes, 114 comments). Despite lower tok/s (27 vs 32), Gemma's dramatically better code efficiency resulted in a functional game versus Qwen's failure. This challenges the 'bigger is better' assumption for practical coding tasks.
2026-04-23 / reddit-researcherQwen 3.6 Quality Drives Hardware Discussion — M5 Max 128GB Demand as Local Models Approach Cloud QualityA 76-upvote post on r/LocalLLaMA asking whether to upgrade to a 128GB M5 Max generated 113 comments — a 1.49 comment-to-score ratio, the highest in today's dataset. The discussion reflects a practitioner tipping point: local model quality is now close enough to cloud APIs that builders are making $4,000+ hardware investments to run models locally, shifting the cost calculus from API bills to capital expenditure.

Graph relationships

BENCHMARKED AGAINST
M3 Max
Practitioner benchmarked M5 Max vs M3 Max inference performance
Source finding
USES
Qwen 3.5
Qwen 3.5 model benchmarked on M5 Max MacBook Pro
Source finding
BENCHMARKED AGAINST
Qwen3.5-397B
Qwen3.5-397B inference was optimized and tested on Apple M5 Max.
Source finding
USES
Qwen 3.5 -> M5 Max
Qwen 3.5 model benchmarked on M5 Max MacBook Pro
Source finding
BENCHMARKED AGAINST
Qwen3.5-397B -> M5 Max
Qwen3.5-397B inference was optimized and tested on Apple M5 Max.
Source finding

Source trail

Liquid AI h3.c Muse Glimmer unsloth/Muse-Glimmer-30B-GGUF Meta's technical blog posted on X independent public harness r/LocalLLaMA user JustVugg/colibri Hardware Corner Qwen3.6-27B Simon Willison tested

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…