r/machinelearningnews

NVIDIA released a 64GB configuration of DGX Spark, its GB10 Grace Blackwell desktop system, available October 23 from Acer, ASUS, Dell, Gigabyte, HP and MSI.

• Up to 1 petaFLOP FP4 (with sparsity), 20-core Arm CPU
• 64GB coherent unified LPDDR5x, 273 GB/s memory bandwidth
• Fits 30–35B class open models: Qwen3.8-27B (~13.5GB at 4-bit), Muse Glimmer (~17GB quantized), Nemotron 3.5 Lightning (30B-A3B, NVFP4)
• 2 units over ConnectX-7: 128GB pooled, 546 GB/s combined
• NVIDIA says 2 × 64GB delivers up to 1.7x the performance of 1 × 128GB Spark
• NVIDIA Sync's Cluster Assistant configures up to 4 systems

Full breakdown: https://www.marktechpost.com/2026/10/02/nvidia-announces-dgx-spark-64gb-a-1-petaflop-grace-blackwell-desktop-for-local-ai-agents-fine-tuning-and-inference/

Product page: https://www.nvidia.com/en-us/products/workstations/dgx-spark/

Clustering with NVIDIA Sync: https://build.nvidia.com/spark/connect-to-your-spark/sync

Technical details: https://blogs.nvidia.com/blog/local-ai-dgx-spark-64gb-sync/

Cloudflare released Clef and Clef-flash today, the first models trained by its Workers AI team. They are decision models in the same family as TypeSafe's Jev: you pass a state plus a schema of typed questions (noul, choice, score), and you get back a probability for every allowed option. No free-form text, no output parsing.

Architecture

• Clef is post-trained from Qwen3.8-27B, Clef-flash from Qwen3.5-9B. Both keep the vision encoder, so the state can include images and video.
• The frozen backbone runs a single prefill-only pass. A small transformer "joint schema head" then routes evidence to each question, lets fields cross-attend, and scores all options jointly. The decision step is non-autoregressive.
• Training: rank-256 LoRA adapters trained jointly with the routing head, label-smoothed cross-entropy plus a Brier loss for calibration, and RLCD as a secondary objective.

Numbers from Cloudflare's own Decision Index run

	Clef	Clef-flash	Jev
Median latency	209.3 ms	38.8 ms	524.1 ms
BANKING77 (macro-F1)	94.20	90.93	79.74
CLINC150+OOS (macro-F1)	97.43	66.77	89.27
GPQA Diamond	48.0	51.0	78.3
MMLU-Pro	65.9	65.3	82.7

So it wins clearly on classification and tool-routing style tasks, and loses clearly on knowledge-heavy reasoning. All numbers are vendor-reported with no independent replication yet.

Running it

• Hosted on Workers AI: $0.24 / M input tokens (Clef), $0.09 / M (Clef-flash), 65,536-token context.
• Weights on Hugging Face under Apache 2.0. The model cards say they tested on a single H200 in BF16 with a custom loader.
• The API follows TypeSafe's System One format, so Jev code ports by changing the endpoint and model name.

Model: https://huggingface.co/Cloudflare/clef

Clef-flash: https://huggingface.co/Cloudflare/clef-flash

Cloudflare blog: https://blog.cloudflare.com/clef-decision-models/

Our full write-up: https://www.marktechpost.com/2026/10/01/cloudflare-releases-clef-and-clef-flash/

AWS's Strands Agents team released Strands Decider 2B today. It's not a chat model. It never generates text. You give it a state plus typed questions, and it returns one of three things, each with a calibrated confidence:

• choice: pick 1 of N options
• noul: a yes/no probability
• score: a level on an ordered rubric

How it works

They take Qwen3.5-2B-Base, remove the language-modelling head, and replace it with a ~1M-parameter pointer head. That head scores each option's last-token hidden state against the hidden state at an <answer> position. One forward pass, no decoding loop, and it can only ever answer with an option you supplied. The torso gets a rank-16 LoRA. Because the head has no per-option weights, labels come from the request and there's no cap on option count.

Asking several questions about the same text is cheap: the state is read once and each extra question only adds its own tokens.

Numbers (v19 checkpoint, from the repo)

• JevBench v1 public set: 0.723 accuracy (167/231)
• Brier 0.342, ECE 0.052
• Tiers: easy 1.000 / standard 0.875 / hard 0.505
• RTX 3090: 115 ms median, 299 ms p95 per question
• M3 Pro: 153 ms warm median (under 300 tokens)
• On unseen short tasks, answers at 0.9+ confidence were right about 95% of the time

Context and caveats worth knowing

• This is the open, self-hostable counterpart to the class TypeSafe kicked off with Jev, which is a closed API.
• On the JevBench v1.4.2 board, v19 was 3rd of 33 in the 2B class. Mapika's decider-2b (same Qwen3.5-2B torso, different recipe) has a newer v11 that the Strands repo itself says scores 175/231, 8 tasks ahead.
• Long multi-step documents are the weak spot, and calibration was fitted on short classification tasks. The authors say to measure thresholds on your own traffic.
• The bundled HTTP server binds to localhost with no auth, so put something in front of it for anything real.

Intended uses: model routing, tool selection, tool-argument checking, triage, guardrails, cheap evals, and hybrid agents where the LLM handles hard calls and the decider handles rote ones.

Our full write-up with an interactive explainer: https://www.marktechpost.com/2026/10/01/aws-strands-labs-releases-strands-decider-2b/

Blog: https://strandsagents.com/blog/introducing-strands-decider/

GitHub: https://github.com/strands-labs/strands-decider

Weights (HF): https://huggingface.co/StrandsAgents/strands-decider-2B-hobson-v19

JevBench comparison in the repo: https://github.com/strands-labs/strands-decider/blob/main/evaluation/jevbench.md

Introducing AstaBrief 8B, an open model that turns complex research questions and literature excerpts into cited reports.

Run it locally on your own hardware, with open weights and training data you can inspect & build on. 👇

AstaBrief powers Fast mode in Asta’s Generate a report feature. It writes reports in one pass, skipping intermediate steps that summarize & group the retrieved excerpts so researchers can get a preliminary report sooner.

We built AstaBrief on Qwen3-8B using queries from ScholarQA & our earlier literature-synthesis systems. We removed bot/test traffic & very short prompts and used an LLM to filter non-English or non-scientific requests & prompts with personal info, leaving 90K queries.

From selected queries in the 90K pool, we generated cited reports. Quality filtering left 39.5K examples for supervised fine-tuning (SFT). We used separate queries to build ~6K report pairs where two judge models agreed, then trained with direct preference optimization (DPO).

AstaBrief is built for speed & quality. Across Asta’s full pipeline, Fast mode averages 51.1s per report vs. Claude-powered Thinking mode’s 178.5s—about 3.5× faster. And in our evaluations, AstaBrief is competitive with Thinking mode & DR Tulu on answer + citation measures.

Our example repo provides a starting point for generating reports from your own PDFs locally. For more info, read our blog.

🤗 Download AstaBrief 8B: https://huggingface.co/allenai/AstaBrief_8B

🌐 Try Fast mode: https://asta.allen.ai/

💻 Example repo: https://github.com/allenai/ai2-scholarqa-lib/tree/main/api/scholarqa/lite

📊 Training data: https://huggingface.co/collections/allenai/astabrief
📝 Blog: https://allenai.org/blog/astabrief