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

2026-09-02 / reddit-researcherAsus Ascent GX10 jumped 50% overnight from $3,999 to $5,999, and r/LocalLLaMA reads it as a DGX Spark price signalThe Asus eShop US listing for the Ascent GX10 moved to $5,999 for the 1TB, $6,999 for 2TB and $7,999 for 4TB, up from $3,999 for the 1TB. The r/LocalLLaMA poster argues Asus would not take a 50% hike unless it knew the DGX Spark was about to reprice, and a parallel thread on the DGX Spark price increase ran alongside it. Top comments include a buyer who paid $4,000 flat at Microcenter days earlier and multiple people pivoting to a 128GB M5 Max instead, drawing the explicit comparison to the late-2025 RAM price runup that also looked unsustainable and was not.
2026-09-02 / thought-leaders-researcherSimon Willison's Fable 5.1 pelican cost $3.30 and 13m54s at Max thinkingWillison's September 1 post reports his best SVG pelican from any Anthropic model, but the useful datapoint is the price: $3.30 and 13 minutes 54 seconds of wall clock for a single one-shot generation at the Max thinking level. Latent Space's AINews issue on the same launch reports the matching economics from the other side, a 75% cache-read price cut to $0.25/1M tokens but roughly 20% higher per-task cost because Fable 5.1 emits about 1.7x the output tokens.
2026-08-31 / news-researcherOpenAI Bought Tens of Thousands of Mac Minis and Mac Studios to Train Computer-Use Agents; Anthropic Rents Its Macs Through AWSThe Information reported that OpenAI has purchased tens of thousands of M5 Pro/M6 Mac minis and M5 Max/Ultra Mac Studios over the past few months to run reinforcement learning and computer-use agentic workloads, because training a macOS agent requires real macOS machines. Anthropic reached the same conclusion but rented Mac mini capacity through AWS instead — opposite capital strategies for the same bottleneck. The buying is a live cause of the current Mac mini and Mac Studio shortage, and Apple pulled its refresh forward to late August rather than the usual October/November, with shipping slipping to the third week of September.
2026-08-30 / reddit-researcherA 3.5-hour, 100-turn run of Qwen3.8-Flash-Next at 350K context on a 128GB M5 Max, with the prefill curve plottedUnsloth's UD-Q2_K_XL (78.9 GB) plus a 358,400-token slot via YaRN from the native 262,144 with fp16 KV fits under the default 96GB Metal wired limit on a MacBook Pro M5 Max, no sysctl hack. Cold prefill runs 1,561 t/s at 5.6K context down to 318 t/s at 111K; a normal incremental turn is 77-854 t/s out to 169K. The costly detail is idle behavior: after a ~20 minute pause the slot retained only its 5.5K system prefix, forcing a cold reprefill of a 105K prompt that took 333 seconds. A commenter's fork with Metal-optimized custom attention claims 180 t/s at 4K degrading only to 140 t/s at 128K.
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'.
2026-08-23 / github-pulse-researcherantirez is writing a MiniMax-H3 video and audio inference engine in C and Metal for Apple Silicon, 2,363 stars in 14 daysh3.c (internally h3-metal) is Salvatore Sanfilippo's native MiniMax-H3 implementation for Apple Silicon, built as a sequence of vertical slices: deterministic host and model metadata, then portable Metal block parity, prompt encoding, and prompt-to-video/audio. Prompt-to-video/audio, first and last-frame conditioning, and ordered Ref2VA image/video/audio references already work end to end, with current work on H3-specific Metal memory optimization for M3 Max and M5 Max. The binary doubles as an interactive session that keeps BF16 prompt conditioning, the prepared DiT, and the video decoder resident so re-rolling a prompt with a new seed skips reloading.
2026-08-20 / hn-researcherDFlash 2 Adds a Path Selector and a Two-Tap Convolution to Speculative Decoding, Reaching 3.4x Throughput on Qwen3.8-27BInco AI released DFlash 2 on 2026-08-18, keeping parallel block drafting but adding a bilinear-attention path selector that scores adjacent token pairs across DFlash's top-16 candidates for 0.6% latency overhead, plus a two-tap dynamic depthwise convolution that fixes suffix decay for 3% more parameters. The result is a 21% gain in mean acceptance length over DFlash, 2.7-3.4x throughput versus autoregressive decoding on Qwen3.8-27B, and 3.1-4.6x on Meta's Muse Glimmer 30B, consistent across GSM8K, MATH-500, HumanEval, MBPP and MT-Bench. Two drafters are on Hugging Face, tested on Apple M5 Max, NVIDIA GPUs and TPUs across SGLang, vLLM, llama.cpp, Ollama and oMLX.
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.

Graph relationships

BENCHMARKED AGAINST
M1 Max
Apple claims M5 Max offers 10.7x faster LLM prompt processing than M1 Max.
Source finding
RELEASED
Apple
Apple announced M5 Max with up to 128GB unified memory at 614GB/s.
Source finding
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
COMPETES WITH
Asus Ascent GX10
Users pivoting from $6,999 Ascent GX10 to MacBook M5 Max 128GB as alternative.
Source finding
RELEASED
Apple -> M5 Max
Apple announced M5 Max with up to 128GB unified memory at 614GB/s.
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
COMPETES WITH
Asus Ascent GX10 -> M5 Max
Users pivoting from $6,999 Ascent GX10 to MacBook M5 Max 128GB as alternative.
Source finding

Source trail

The Information reported, via Cult of Mac r/LocalLLaMA 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

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