Entity trail

Qwen3.5-122B

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

Briefing refs

Findings

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Sources

Corpus findings

2026-05-12 / reddit-researcherDGX Cooled with Tap Water Runs Qwen3.5-122b-A10B at 18.77 Tokens/Second with 80K ContextA r/LocalLLaMA user shared their unconventional DGX cooling solution using tap water, keeping GPU temperatures below 68°C at 95% utilization. The setup runs Qwen3.5-122b-A10B at Q6_K precision with 110GB memory usage, achieving 18.77 tokens/second for continuous inference at 80K context. The 388-upvote post highlights the creative hardware hacking culture in the local AI community.
2026-04-10 / vibe-coding-researcherQwen3.5-122B Hits 198 tok/s on 2x RTX PRO 6000 Blackwell — Full Methodology PublishedA developer achieved 198 tokens/second running Qwen3.5-122B on a budget 2x RTX PRO 6000 Blackwell setup, with raw JSONs, launch commands, and full methodology published publicly. The 112-upvote r/LocalLLaMA post with 179 comments shows intense community interest in Blackwell inference performance. For builders evaluating local setups, this confirms that consumer Blackwell GPUs can run 122B models at speeds previously requiring enterprise hardware.
2026-04-03 / hn-researcherAMD Launches Lemonade: Open-Source Local AI Server with GPU+NPU Acceleration, 521 HN PointsAMD's Lemonade is an Apache 2.0 open-source local AI platform providing OpenAI-compatible APIs for text, image, vision, and speech across NVIDIA GPUs, AMD Ryzen AI NPUs, and CPU backends. HN users on Strix Halo report 35-60 tok/s on large models like Qwen3.5 122B, with Lemonade beating Ollama by ~30 seconds on identical tasks. The 2MB C++ backend auto-configures for hardware and bundles llama.cpp, Stable Diffusion, and Whisper — though HN commenters flag NPU underperformance and ROCm stability issues as real limitations.
2026-03-29 / reddit-researcherOld MI50 GPUs Resurrected: Merged llama.cpp Forks Run Qwen3.5 122B on 4x MI50 16GB CardsA practitioner merged gfx906 and Turbo3 forks of llama.cpp and successfully ran Qwen3.5 122B on four AMD MI50 16GB GPUs (271 upvotes, 104 comments). The MI50, a data center GPU from 2018, gets new life through community fork work. This demonstrates the local inference community's ability to extend hardware lifecycles far beyond manufacturer support, running frontier-class models on 7+ year old hardware.
2026-03-28 / reddit-researcherM5 Max vs M3 Max Inference Benchmarks — Qwen3.5 Performance Comparison Across Three Model SizesA practitioner ran identical benchmarks on both 16-inch MacBook Pros (M5 Max and M3 Max, both 40 GPU cores, 128GB unified memory) across Qwen 3.5 122B-A10B MoE, 35B-A3B MoE, and 27B dense models using oMLX v0.2.23. The 81-upvote post with 31 comments provides the first direct M5 Max vs M3 Max inference comparison, giving local model practitioners concrete data on whether Apple's latest silicon justifies the upgrade for LLM workloads.
2026-03-27 / vibe-coding-researcherTip: Homelab MoE Consolidation — One Qwen3.5 122B MoE Replaces Three Specialized Models on Strix Halo 128GBA practitioner running Proxmox with LXC containers consolidated from three separate models (coding, reasoning, chat) to a single Qwen3.5 122B MoE on a Strix Halo setup (Ryzen AI MAX+ 395, 128GB RAM, 96 GiB shared GPU via Vulkan/RADV). The MoE architecture with 40B active parameters matches or exceeds the specialized models across all tasks while simplifying infrastructure. With 79 upvotes and 41 comments, the post includes specific llama-server configurations and benchmark comparisons — a practical guide for anyone running local inference infrastructure.
2026-03-27 / vibe-coding-researcherTip: Qwen3.5 ROCm vs Vulkan Benchmarks on AMD GPUs — ROCm Wins Prompt Processing, Context Size Matters More Than BackendA practitioner benchmarked Qwen3.5 (35B MoE, 27B Dense, 122B MoE) across Apple Silicon and AMD GPUs, finding ROCm outperforms Vulkan for prompt processing on AMD hardware. The surprising result: context size has a larger impact on throughput than the choice of compute backend. MacBook Pro M-series performance is competitive with AMD desktop GPUs for the smaller models. With 71 upvotes and 46 comments, the post includes specific tok/s numbers across all configurations — useful for anyone choosing between Apple and AMD for local inference.
2026-03-27 / vibe-coding-researcherAutoBE Function Calling Harness: TypeScript Compiler + Lenient Parsing Takes Qwen from 6.75% to 100% Build SuccessPresented at Qwen Meetup Korea (invited by Qwen team), AutoBE's function calling harness combines three techniques: Typia compiler-level TypeScript-to-JSON-Schema generation, lenient JSON parsing that recovers from markdown blocks and unclosed brackets, and structured inline validation feedback (❌ comments on exact paths). Results: Qwen3.5-397B, 122B, and 27B all hit 100% compilation success; Qwen3-Coder-Next goes from 6.75% first-try to 99.8% with the harness. The key insight: precise path-level feedback causes models to fix only broken parts without rewriting functional code.
2026-03-22 / reddit-researcherQwen3.5-122B-A10B Uncensored Aggressive GGUF: Full Refusal Removal on Flagship MoEThe community released Qwen3.5-122B-A10B 'Aggressive' as GGUF — the largest Qwen3.5 MoE with all safety filters completely stripped while preserving the original personality without alteration. The 256-upvote post with 93 comments signals strong practitioner demand for uncensored frontier-scale local models, distinct from earlier Qwen uncensored releases that applied subtler modifications. This makes the 122B flagship effectively unrestricted for local deployment.
2026-03-19 / vibe-coding-researcherQwen3.5-Claude-4.6-Opus-Reasoning-Distilled-v2 Released — 40B and 122B VariantsA new v2 distillation of Qwen3.5 targeting Claude Opus 4.6 reasoning patterns has appeared on HuggingFace with 151 upvotes on r/LocalLLaMA. Available in 40B and 122B parameter counts with 'reg,' 'uncensored' (Heretic), and 'Rough House' variants. This follows the original Qwen3.5 Opus distillation and targets locally-runnable Opus-level reasoning quality at reduced infrastructure cost.
2026-03-19 / reddit-researcherM5 Max 128GB Three-Way 120B Shootout: Nemotron-3 Super Beats GPT-OSS 120B and Qwen3.5 122BA practitioner ran Nemotron-3 Super (Q4_K_M), GPT-OSS 120B (MXFP4), and Qwen3.5 122B (Q4_K_M) on M5 Max 128GB and found Nemotron-3 Super wins on both quality and speed, with GPT-OSS 120B second and Qwen3.5 122B third. The post (52up, 52cmts — 1.0 comment-to-score ratio indicating deep technical engagement) is the first published three-way benchmark for the M5 Max against this generation of 120B models, giving local builders a concrete hardware/model pairing guide.
2026-03-17 / vibe-coding-researcherKrasis LLM Runtime: 8.9x Prefill, 10.2x Decode vs llama.cpp on Qwen3.5-122B Single RTX 5090A new LLM runtime called Krasis claims 8.9x prefill and 10.2x decode speedup over llama.cpp running Qwen3.5-122B on a single RTX 5090 with minimal RAM. The author posted a correction confirming the higher decode figure. If benchmarks hold under third-party validation, this represents a significant local inference efficiency breakthrough for large models on consumer hardware.

Graph relationships

USES
AMD MI50
Qwen3.5 122B successfully ran on four AMD MI50 16GB GPUs via merged llama.cpp forks.
Source finding
USES
Krasis
Krasis demonstrates speedups running Qwen3.5-122B on RTX 5090
Source finding
BENCHMARKED AGAINST
Nemotron 3 Super
Nemotron-3 Super outperforms Qwen3.5 122B on M5 Max 128GB.
Source finding
RELEASED
Alibaba
Uncensored Qwen3.5-122B GGUF released to community
Source finding
USES
Krasis -> Qwen3.5-122B
Krasis demonstrates speedups running Qwen3.5-122B on RTX 5090
Source finding

Source trail

r/LocalLLaMA r/LocalLLaMA AMD / Hacker News r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA

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