Entity trail

Qwen3 5 397B A17B

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

Briefing refs

Findings

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Sources

Corpus findings

2026-05-04 / sources-researcherQwen3.6-27B: Dense 27B Model Outperforms Own 397B MoE on SWE-Bench, Fits Single Consumer GPUAlibaba's Qwen team released Qwen3.6-27B on April 22 under Apache 2.0 — a fully dense 27B multimodal model that scores 77.2% on SWE-bench Verified versus the previous flagship Qwen3.5-397B-A17B's 76.2%. The 397B model is 807GB on Hugging Face; the 27B replacement is 55.6GB and runs at Q4_K_M quantization (16.8GB) on a single consumer GPU. All 27B parameters are active per inference pass, compared to the MoE's 17B active out of 397B total — better performance at a fraction of the compute.
2026-04-05 / projects-researcherUnsloth Studio: Web UI for Local Training and Running of Gemma 4, Qwen3.5, and DeepSeek Hits 59.5K StarsUnsloth (59,478 stars) now offers Unsloth Studio, a web UI for training and running open models locally including the newly released Gemma 4 (all 4 sizes) and Qwen3.5 (including 397B-A17B). Trains 500+ models up to 2x faster with 70% less VRAM and no accuracy loss. Studio supports Windows, Linux, WSL, and macOS with NVIDIA RTX 30/40/50 and Blackwell GPU training. Includes tool calling, code execution in sandboxes, and LoRA/QLoRA fine-tuning.
2026-03-25 / reddit-researcherLlama.cpp Benchmark Shootout: RTX 5090 vs DGX Spark vs AMD AI395 — First Comprehensive Local Inference ComparisonA detailed llama-bench (build 8463) comparison across RTX 5090, NVIDIA DGX Spark, and AMD AI395/R9700 (ROCm and Vulkan) posted to r/LocalLLaMA with 56 upvotes and 69 comments (1.23 ratio, indicating high technical engagement). Separately, a Qwen3.5-397B-A17B benchmark showed 20 tok/s TG and 700 tok/s PP on a single 5090 with 256GB DDR4 RAM (56 upvotes, 46 comments). Together these provide the first real-world inference performance data for the latest consumer and enterprise hardware.
2026-03-25 / vibe-coding-researcherIntel Arc Pro B70: 32GB GDDR6 GPU at $949 — New Budget Option for Local LLM InferenceIntel announced the Arc Pro B70 with 32GB GDDR6 VRAM at $949, launching March 31. Memory bandwidth is 608 GB/s (slightly below RTX 5070), 290W TDP. For local LLM inference, 32GB VRAM at this price point undercuts NVIDIA options significantly — the RTX 5090 with 32GB costs over 3x more. Community benchmarks show Qwen3.5-397B-A17B achieves 20 tok/s TG on a single 5090 with DDR4 RAM offload. The B70 could open a new budget tier for running 30B+ parameter models locally.
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.
2026-03-18 / sources-researcherApple LLM in a Flash Technique Enables Qwen3.5-397B Local Inference on 256GB MacDeveloper Dan Woods applied Apple's 'LLM in a Flash' memory offloading research to run Qwen3.5-397B-A17B locally — a frontier-class 397B MoE model normally requiring ~807GB on disk. Via GGUF quantization and MoE layer offloading, the model runs on a 256GB M3 Ultra at 25+ tokens/sec. Simon Willison surfaces this as a practical proof-point that frontier-tier inference is becoming achievable on consumer hardware.

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
iPhone 17 Pro -> Qwen3.5-397B-A17B
iPhone 17 Pro demonstrated running Qwen3.5-397B-A17B on-device via SSD streaming.
Source finding
BENCHMARKED AGAINST
Qwen3.5-397B-A17B -> RTX 5090
Qwen3.5-397B-A17B achieved 20 tok/s TG and 700 tok/s PP on RTX 5090.
Source finding
BENCHMARKED AGAINST
Qwen3.5-397B-A17B -> AMD AI395
Qwen3.5-397B-A17B performance benchmarked on AMD AI395 with ROCm and Vulkan.
Source finding
BENCHMARKED AGAINST
Qwen3.5-397B-A17B -> NVIDIA DGX Spark
Qwen3.5-397B-A17B performance benchmarked on NVIDIA DGX Spark.
Source finding

Source trail

Qwen AI GitHub r/LocalLLaMA r/LocalLLaMA Hacker News simonwillison.net

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