AI model
27B dense, 64-layer hybrid-attention vision language model released with an Apache 2.0 licence, with only 16 full attention layers keeping a KV cache.
Tech Against Terrorism found that stripped copies of new open-weight models now appear online in under three days, against more than 24 days in 2024. Teams building on open models should treat built-in refusals as a default setting they cannot rely on.
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Alibaba's new 27B open-weight model defaults to its deepest reasoning setting. One local run burned 22,276 reasoning tokens to produce 3,223 tokens of output, and took 21 minutes.
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Oki is taking $59 reservations for Oki Home, a $1,799 computer that runs a 27-billion-parameter AI model on personal data at home. How much of that data stays in the house depends on which outside services each owner connects to it.
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NVIDIA's 64GB DGX Spark goes on sale from six PC makers on October 23 at $4,999, about $2,000 to $4,000 below in-stock 128GB units today. Two clustered units cost more than one 128GB machine, so the purchase makes most sense for agents on models that fit in 64GB.
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IntelligenceLab and Maryland researchers say rewriting scaffolded agent runs lifted Qwen-3.8-27B to 74.2% on Terminal-Bench 2, versus 53.4% from raw traces. The result argues for replaying harness-assisted wins in a plain shell before training on them.
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g factor's Qwen 3.8 27B benchmark has Together AI fastest at one stream, at 189.61 tok/s, while four of five engines finish within about 10% at 64 streams. Choosing a provider from these numbers starts with knowing how many streams the deployment will run at once.
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IBM's 3B, 8B and 30B dense models all get a thinking switch and native tool calling, but only the two larger ones get agentic RL, and the tuning mixture leans hard on software engineering.
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Inferact measured 709 output tokens a second on 16 Ironwood chips against 452 on 16 GB200s, at low concurrency with speculative decoding. The engineering worth reading is the hand-written memory schedule underneath.
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One MIT-licensed proxy on localhost is enough to serve OpenAI's own desktop client from self-hosted models. The models that fail there fail on Codex's tool-call format. One of three tested got lost.
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Alibaba's 27B scores 52 on the Artificial Analysis index from a 17GB quantized file. Filling its 262,144-token window needs roughly 16 GiB of KV cache on top of that, so the file size is the smaller half of the sizing question.
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Alibaba's 27B model fits a 32GB card with 15GB to spare. Tom's Hardware still had to pick between llama.cpp's full 262K window at half-hour prefill and a supported vLLM deployment capped at 32K.
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Portable Computer runs Perplexity's agent on 27B models on your own hardware. Paid tiers only, Linux first, Windows in September, and a 24GB VRAM floor most desktops miss.
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Portable Computer runs the agent's control plane on device and charges only when a task goes to the cloud. The part that decides when that happens is also the part nobody has priced or locked down.
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Alibaba's new 27B model defaults reasoning_effort to xhigh. Simon Willison measured 22,276 reasoning tokens and 21 minutes for one SVG that took two minutes with reasoning off.
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Qwen 3.8 27B ran on a MacBook Pro from a 17GB GGUF and spent 21 minutes on one SVG. Licensing and access stopped being the blocker; latency and KV cache budgeting became the job.
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Two releases, two licences. Only the 27B is Apache 2.0, and because just 16 of its 64 layers keep a KV cache, long context costs a quarter of the usual memory.
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