Recent Blog Posts
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GLM-5.3-Flash Day Two: DFlash 2 Attacks the Decode Path While 1M-Context KV Moves Onto Two DGX Sparks
by Chris DePuy / August 28, 2026 A day after Z.ai opened weights on GLM-5.3-Flash, the desk-side community pushed the model past what I thought was its practical limit on small hardware. Two bottlenecks — the decode path and the KV cache — were attacked in parallel, and both moved in the same 24 hours.…
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Grok Bot, Two Weeks In: The Always-On Agent Workforce Gets a Shared Computer and a Shared Security Boundary
by Chris DePuy / August 29, 2026 xAI launched Grok Bot in early beta on August 11, and in the two weeks since, the community has made the platform’s ambition concrete faster than the company’s own launch materials do. The product is not another chat interface — a Bot gets its own cloud computer, signs…
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DeepSeek V4 Flash 0731 on the Multi-Node DGX Spark: From One Pair to an Eight-Node Cluster
by Chris DePuy / August 4, 2026 When DeepSeek shipped the official DeepSeek-V4-Flash-0731 weights on July 31, the community benchmarks that followed concentrated on single and dual DGX Spark units. Over the past several days a more interesting pattern has emerged as the same checkpoint moves up the cluster ladder, from a single GB10 to…
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Kimi K3 Open at 2.8 Trillion Parameters Closes The Gap
by Chris DePuy / July 28, 2026 On July 27, Moonshot AI made good on its promise: the full weights of Kimi K3 — at 2.8 trillion total parameters the largest open-weight model ever released — went live on Hugging Face under a Modified MIT license. The release includes the model’s technical report and a…
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Poolside’s Laguna S 2.1 118B Punches Above Its Weight
by Chris DePuy / July 28, 2026 This week, the local AI community has been running Poolside’s latest open-weight model, Laguna S 2.1, on everything from single DGX Sparks to 3090 Quad workstations. Released on July 21, Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token, a 1M-token…
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1T Inkling Model at 1-bit is 86% smaller
AI-generated concept image of model compression via 1-bit quantization. Thinking Machines Lab’s Inkling — a 975B-parameter (41B active) multimodal with 1M context, vision, and audio (HF model card) — launched day one with a dynamic 1-bit GGUF courtesy of Unsloth. The Compression Format Size Vs. Full BF16 (full) 1.9 TB — NVFP4 ~490 GB 74%…
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Kimi K3: 2.8 Trillion Parameters, Open Weights — The New Frontier Ceiling
AI-generated concept image of a massive trillion-parameter AI supercomputer. Moonshot AI released Kimi K3 on July 16 — the largest open-weight model ever published at 2.8 trillion total parameters (Moonshot AI announcement). This isn’t a deskside model. It’s the new ceiling the quantization community needs to tackle. Architecture at a Glance Spec Value Total parameters…
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GLM-5.2 on 2× DGX Spark: First Non-DeepSeek DSpark Port Hits 19.8 tok/s
AI-generated concept image of dual-processor inference acceleration. Red Hat AI did something the community wasn’t sure was possible: they adapted DSpark speculative decoding to a non-DeepSeek model. GLM-5.2-FP8 now runs with a DSpark draft model, and the numbers are real. The full validation and model weights are published on HuggingFace (RedHatAI/GLM-5.2-speculator.dspark). The Breakthrough DSpark was…
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DeepSeek V4 Flash DSpark on 2× DGX Spark: ~60-67 tok/s with Speculative Decoding
For local inferencing, here is a setup that has proven to be quite stable and fast. It’s the 2x DGX Spark running DeepSeek V4 Flash. It keeps the model running locally and, I’d say, is near Frontier, and doesn’t get lost often when using agents. It’s pretty good at planning, too. The reviews quote ~40…
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SGLang vs vLLM vs llama.cpp vs Atlas — The Inference Engine Shootout for DGX Spark Clusters
SGLang vs vLLM vs llama.cpp vs Atlas — The Inference Engine Shootout for DGX Spark Clusters The NVIDIA DGX Spark (GB10) has transformed from a curiosity into a genuine building block for local AI infrastructure. What began as a 128GB unified-memory desktop appliance has, through community effort and NVIDIA’s own software maturation, become a node…
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