Daniel B. - AI & Tech

Qwen just leaked a 120B model that can see, hear and think... "qwen4-omni-120b-think" showed up in the test files of Qwen's own code, along with a 1M token context window for Qwen4 and that's on top of the lineup we're reportedly getting: > Qwen4-Max > Qwen4-Plus > Qwen4-Flash > Qwen4-27B Qwen's Omni models handle text, images, audio and video all in one model, so a 120B one with reasoning would be huge Alibaba Cloud's CTO reportedly said "next week" on stage last Wednesday, so this could all drop any day Qwen4-27B is going on my 3090 the second it does also I found a video of us all migrating to Qwen4-27B:

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GTA 6 is 210 GB. Most local AI models are smaller than that. People will happily download a 210 GB game in seconds and then complain a local model is "too big." If you're buying GTA 6, you can afford 32 GB of RAM. 🎮🤖

BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list. Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%. Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything. Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO. h/t @OliverMolander Would you move your team off frontier APIs if the bill dropped 90%?

A single RTX 3090 is reportedly pushing 1,035 tokens/s of aggregate throughput while serving 64 concurrent Qwen3.8-27B sessions. The interesting part? This is a 24GB GPU running with a 250W power limit. No multi-GPU setup. No expensive inference accelerators. Just one RTX 3090, vLLM and an open-source project called HyperQwen. The reported configuration • GPU: NVIDIA RTX 3090 (24GB VRAM) • Power limit: 250W • Model: Qwen3.8-27B • Inference engine: vLLM • Single-session generation: Approximately 127 tokens/s • 64 concurrent sessions: Approximately 1,035 tokens/s combined • Longest tested context: Up to 150K tokens There are two numbers worth paying attention to. The 127 tokens/s figure represents generation speed for a single session. The 1,035 tokens/s figure is the combined output across 64 simultaneous sessions. In other words, one conversation is not receiving 1,035 tokens every second. The server is distributing available GPU resources across multiple requests and accumulating the output from all of them. That distinction changes how we should look at this result. Local inference is also about serving multiple users Most local LLM benchmarks focus on one question: How quickly can my GPU generate a response? But developers building AI applications have another problem to solve. What happens when several agents and applications need the same model at once? Imagine one machine running: • A coding agent modifying a repository. • A research agent gathering information. • A document assistant processing files. • A code reviewer checking generated changes. • Other applications sending requests to the same model. Running a separate model instance for every application would be wasteful. A shared inference server allows these applications to connect to one endpoint and use the same model infrastructure. That’s the area HyperQwen is exploring. Why more concurrent sessions increase throughput A GPU serving one request may have processing capacity that isn’t fully utilised throughout generation. With multiple requests arriving, vLLM can schedule their execution together through continuous batching. Instead of treating every conversation as an isolated workload, the server combines work from multiple requests to keep the GPU busier. Efficient KV-cache management also helps the system handle concurrent sessions without wasting as much memory on attention state. There is a trade-off, though. Higher aggregate throughput doesn’t automatically mean faster responses for each individual user. As more sessions compete for compute and memory, per-session generation speed and latency can change. The objective is to increase the total amount of work the server completes, not necessarily make every conversation faster. Getting started HyperQwen provides two Docker Compose profiles: Single-session mode 
docker compose --profile single Batch mode 
docker compose --profile batch The initial setup also requires some preparation. The first launch reportedly downloads around 9.5GB of Docker images, followed by a local model requantisation process involving approximately 20GB of data. That initial work needs to finish before running the inference benchmarks. Project: HyperQwen
GitHub: syv-ai/HyperQwen Why this matters for developers GPU specifications alone don’t tell us how useful a machine will be for running AI applications. Single-session generation speed measures how quickly one user receives tokens. Aggregate throughput measures how much output the server can deliver across multiple users and applications. Both are important, but they describe different aspects of inference performance. HyperQwen’s reported results show what can be achieved by optimising the serving stack around a relatively affordable, widely available GPU. For developers building local AI services, shared inference environments or multi-agent systems, this is worth exploring.

Bambu Lab H3C launches today. Nine independent PrintTools™ that allow multiple nozzle sizes in a single print. Powered by SwapTek™, Print-to-print tool switch only takes 6 seconds. Multiple toolheads with multiple possibilities. Print, cut, draw and deboss. All on the same machine. We built this machine. Now we want to see what you make with it. -------------------------- To celebrate the launch, we're sending one H3C Combo to a maker ready for it. How to enter: • Follow @BambulabGlobal • Like + Repost with: "Bambu Lab H3C. Tool Changer. Game Changer!" • Comment: Which H3C feature impressed you the most? Winner revealed: Nov 6, 4AM EST Explore the H3C: share.bambulab.com/4yoyNdz

Been focusing on the Desktop app this last week or two. This PR should be welcome news to a lot of you power users. On next full release later this week I think you'll start noticing the difference in the desktop app getting noticeably nicer. Still a long way to go, but we'll get there. Lots of UX and design work coming shortly.

What a disaster atlasum.net/blog/cve-2026-98…

Intel is shifting production away from regular PC CPUs and putting more focus on server chips because AI demand is going crazy. Intel is struggling to keep up with demand. >AI companies are getting priority. >There could be fewer CPUs available for desktop and laptop PCs. >Prices could increase if supply gets tighter. >Intel expects new factories and production capacity to help from late 2027. Intel CEO Lip-Bu Tan described it as one of the most significant supply shortages in the company’s history.

🚨 OPENAI JUST ENTERED THE HARDWARE RACE WITH A $230 AI KEYBOARD! Sam Altman surprised approximately 50 Samsung executives with OpenAI's first branded hardware product, the Codex Micro Keyboard. Here's what makes it different: • Just 13 mechanical keys • Rotary dial adjusts AI reasoning depth • Joystick controls coding tasks • LED lights display AI agent activity • Voice commands replace traditional typing • Price: $230, already sold out THE BIGGER STORY: OpenAI is moving beyond chatbots into hardware designed specifically for AI agents. Altman's message to Samsung executives was clear: Simply adopting AI won't create a lasting competitive advantage. Companies must use it to build better products and move faster. For $AAPL, $MSFT, $GOOGL and Samsung investors, this raises an important question: Could AI agents eventually replace traditional software interfaces? THE NEXT COMPUTING REVOLUTION: From typing commands to delegating tasks. From using software to supervising AI. The next trillion-dollar opportunity may be how humans interact with machines. Follow @TMA_MarketIntel for AI, technology and market intelligence.

Q 35B model on an 8GB VRAM laptop just to see if I could... And with 38 tokens pee second... We're making huge strides in local AI. Qwen 3.6-35B-A3B, Unsloth's UD-Q4_K_XL.. On a MSI A15 laptop with an RTX 4060... Local AI is shaping up to shake the industry. This is huge here. #AI #LLM #LocalAI