Entity trail

Flash-MoE

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-15 / arxiv-researcherDeepSeek-V4 Pro/Flash ship as MoE models with 1M context and hybrid attention for cheaper agentic inferenceDeepSeek released DeepSeek-V4-Pro and DeepSeek-V4-Flash, MIT-licensed Mixture-of-Experts models with a 1M-token context window and a hybrid attention mechanism aimed at cutting inference cost for long-running agentic tasks (Pro ~1.6T total / 49B active; Flash ~284B / 13B active). With the highest reported open-weights SWE-bench Verified score (~80.6%) and Flash output around $0.28/M tokens, it is a strong cost/capability option for self-hosted agent stacks.
2026-06-07 / rss-researcherMicrosoft's In-House MAI Coding Models Roll Out to GitHub Copilot and VS CodeMicrosoft's first in-house MAI models from Build 2026 are now reaching developers: MAI-Code-1-Flash, a 5B-parameter coding model in GitHub Copilot and VS Code that Microsoft says beats Claude Haiku 4.5 across core coding benchmarks (+16 points on SWE-Bench Pro, 51.2% vs 35.2%) using up to 60% fewer tokens. MAI-Thinking-1, a 35B-active-parameter MoE with a 256K context window, hits 97% on AIME 2025 and 53% on SWE-Bench Pro (near Opus 4.6) and was trained without OpenAI data — a clear signal Microsoft is reducing its OpenAI dependence.
2026-05-24 / reddit-researcherClaude Code Running on Local LLMs via Ollama — Practitioner Guide With Full SetupA 197-upvote guide on r/ClaudeAI walks through running Claude Code's full agentic workflow with local models via Ollama, reporting surprisingly usable results. The setup requires setting ANTHROPIC_BASE_URL to localhost:11434. Community benchmarks show Qwen3.6:27b hitting 77.2% on SWE-Bench Verified, with GLM-4.7-Flash's MoE architecture (3B active parameters from 30B total) running on 16GB RAM. Official Ollama documentation now includes a dedicated Claude Code integration page.
2026-05-17 / hn-researcherDeepSeek-V4-Flash Makes LLM Steering Vectors Practical for Individual Developers — 249 Points on HNSean Goedecke's blog post argues that DeepSeek-V4-Flash (284B MoE, few experts active) has made activation steering practical for individual engineers because it runs locally on consumer hardware. Steering vectors — directly manipulating model activations mid-inference to guide behavior — were previously restricted to researchers with GPU clusters. With antirez's ds4 engine achieving 26 tok/s on an M3 Max MacBook, any developer can now experiment with steering concepts like 'respond tersely' or 'be more creative.'
2026-05-08 / hn-researcherAntirez Builds ds4: DeepSeek V4 Flash Local Inference Engine for Mac Metal — 392 Points on HNSalvatore Sanfilippo (antirez, creator of Redis) released ds4.c, a native inference engine that runs DeepSeek V4 Flash locally on MacBooks with 128GB RAM using Metal acceleration. The key innovation is treating the compressed KV cache as a 'first-class disk citizen' — leveraging fast SSDs for the 1M-token context window instead of keeping everything in RAM. Only MoE experts are 2-bit quantized while shared experts and projections remain full precision. Hit 392 points on HN.
2026-04-29 / sources-researchervLLM v0.20.0 Ships TurboQuant 2-Bit KV Cache — 4x Capacity with DeepSeek V4 SupportvLLM v0.20.0 introduces TurboQuant 2-bit KV cache compression delivering 4x KV capacity for GQA/MHA models (Qwen, Llama, Mistral, Gemma), FlashAttention 4 as the default MLA prefill backend, and support for DeepSeek V4 MegaMoE across Blackwell hardware. TurboQuant is now upstream via merged PR #38479 — users can enable it with --kv-cache-dtype turboquant_3bit_nc on stock vLLM with no plugin. Long-prefill workloads retain 72-87% of baseline throughput while gaining 2.6-4.9x KV capacity.
2026-04-29 / reddit-researcherInclusionAI Ling-2.6-Flash: 104B MoE Agent Model with 7.4B Active Parameters Trained with Agentic RLInclusionAI released Ling-2.6-flash — a 104B total / 7.4B active MoE model with 262K context window, specifically trained with Agentic Reinforcement Learning for tool use, terminal operations, and multi-step agent tasks. Scores 26 on the Artificial Analysis Intelligence Index (nearly double the median of 13 for comparable open-weight models). Available free on OpenRouter through April 29. Notable as a dedicated agent-optimized open model.
2026-04-22 / reddit-researcherLing-2.6-Flash Confirmed as Stealth 'Elephant Alpha' — Ant Group's 104B MoE Model Processed 100B Tokens Before RevealThe mysterious 'Elephant Alpha' model that topped OpenRouter's trending leaderboard has been unmasked as Ant Group's Ling-2.6-Flash, a 104B total / 7.4B active parameter MoE model built with Agentic RL. The model processed approximately 100 billion tokens on OpenRouter before anyone identified the creator, running at 340 tokens/second. Released under Ant Group's inclusionAI initiative with a 262K context window, it's fully compatible with OpenClaw and positioned as a developer-focused agent backbone.
2026-04-02 / hn-researcherStepFun 3.5 Flash Ranked #1 Cost-Effective Model for OpenClaw Agent TasksStepFun's 3.5 Flash — a 196B-parameter sparse MoE that activates only 11B parameters per token — topped the OpenClaw cost-effectiveness leaderboard across 300 battles. At $0.10/M input and $0.30/M output tokens via OpenRouter, it delivers strong agentic benchmark scores (tau2) and has processed 2.2 trillion tokens on the platform. 168 points and 81 comments on HN signal genuine practitioner interest in cheap-but-capable agent models.
2026-03-31 / rss-researcherLatent Space AINews: 'The Last 4 Jobs in Tech' — llama.cpp Hits 100K Stars, Flash-MoE Runs 397B on MacBookLatent Space's weekend digest highlights local AI hitting a milestone: llama.cpp reached 100K GitHub stars, framing 2026 as the breakout year for local agentic workflows. Flash-MoE on Apple Silicon claims Qwen3.5-397B runs on a 48GB MacBook Pro at 4.4 tok/s using a pure C + Metal engine. The editorial argues useful automation doesn't require frontier-scale hosted models, and companies should own specialized open models on proprietary data rather than rent APIs indefinitely.
2026-03-27 / github-pulse-researcherdanveloper/flash-moe: Pure C/Metal Inference Runs Qwen3.5-397B on MacBook Pro 48GB — 1.8K Stars, 393 HN PointsBuilt by a CVS Health VP of AI, flash-moe is a pure C/Metal inference engine that runs the 397B-parameter Qwen3.5 MoE model on a MacBook Pro with 48GB RAM. Created March 18, it reached 1.8K stars and 393 HN points by demonstrating that frontier-class MoE models can run on consumer hardware without Python dependencies. Directly validates the local inference economics thesis for builders who want to avoid API costs.
2026-03-24 / hn-researcheriPhone 17 Pro Demonstrated Running a 400B Parameter LLM On-Device via SSD-to-GPU Weight StreamingDeveloper @anemll demonstrated an iPhone 17 Pro running Qwen3.5-397B-A17B (2-bit quantized, 17B active params) entirely on-device using Flash-MoE SSD-to-GPU streaming. The MoE architecture activates only 4-10 of 512 experts per layer, avoiding loading all 400B parameters into memory. Performance is 0.6 tokens/second — impractical for daily use but a proof-of-concept that mobile hardware can run frontier-scale models.

Graph relationships

USES
Qwen3.5-397B-A17B -> Flash-MoE
Qwen3.5-397B-A17B uses Flash-MoE SSD-to-GPU weight streaming architecture.
Source finding
USES
Flash-MoE -> Qwen3.5-397B
flash-moe runs Qwen3.5-397B on MacBook Pro with 48GB RAM
Source finding
USES
Flash-MoE -> Qwen3.5
Flash-MoE runs Qwen3.5-397B on 48GB MacBook Pro at 4.4 tok/s
Source finding

Source trail

LLM-Stats Microsoft AI r/ClaudeAI Sean Goedecke / Hacker News GitHub / Hacker News vLLM / GitHub r/LocalLLaMA

Graph sources

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