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generation The image generation connector tool, powered by Black Forest Lab FLUX1.1 [pro] Ultra, enables agents to create images for diverse applications. This feature can be leveraged for various use cases such as generating visual aids for educational content, creating custom graphics for marketing materials, or even producing artistic images. Document library Document Library is a built-in connector tool that enables agents to access documents from Mistral Cloud. It powers the integrated RAG

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You can now refer to image files in the Amp CLI. Paste the file path, drag a file into the terminal, or use file mentions by pressing @ to fuzzy-find the file. Images are useful for prompting with screenshots, designs, and more.

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ove how memory can be efficiently used. Ollama configures causal attention at the individual model level instead of configuring as a group. Examples: Google DeepMind’s Gemma 3 leverages sliding window attention, and Ollama can leverage that to allocate a subset or a portion of the model’s context length to improve performance, and because of the resulting memory efficiency, we can increase the context length of the model on the same system or use the remaining memory for higher concurrency; To u

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ph mode and custom operators; Better user experience and higher performance; Maintenance and enhancement of a stable plugin architecture for appropriate hardware platforms We encourage everyone to try out this new feature! If you have any questions, join the vLLM Slack and participate in the sig-extensible-hardware channel for discussions. 🚀 Acknowledgements This flexible hardware backend plugin mechanism would not have been possible without the efforts of many vLLM contributors. Thus we are dee

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Today, we’re proud to introduce Le Chat Enterprise — a feature-rich AI assistant, powered by our brand new Mistral Medium 3 model . Solving enterprise AI challenges, like tool fragmentation, insecure knowledge integration, rigid models, and slow ROI, it delivers a unified AI platform for all organizational work. Building on the foundation of Le Chat’s productivity tools, the new plan includes: Agent builders Custom data and tool connectors [All features rolling out over the next two weeks.] We’r

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minated by the cost to move the KV cache. To decode a token at step t=1,…,Tt=1,\dots,Tt=1,…,T, we need to load t−1t-1t−1 past keys and values. Therefore, we can approximate the average time for generating TTT tokens as: Putting all of these formulas together, we can lower bound the step time and, consequently, upper bound throughput. Below we are showing the peak theoretical throughput as a function of batch size (up to the maximum batch size that can be fit) for different models running on diff

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s using different hardware configurations. Using 8xH100, vLLM can serve Scout with 1M context and Maverick with about 430K. See more tips below for performance enhancement and leveraging long context. On 8x H100 GPUs: Scout (up to 1M context): Maverick (up to 430K context): The Llama 4 models excel at image understanding up to 8-10 images. By default, vLLM server accepts 1 image per request. Please pass --limit-mm-per-prompt image=10 to serve up to 10 images per request with OpenAI-compatible AP

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n case there is too much selectivity in the query. We don’t want trigger a full graph scan, of course. Other than the HNSW modifications, I wanted a few more things that are more pragmatic and obvious: 1. Threading of all the vector similarity requests. Yeah, that’s new in Redis land, but as much as I believe single thread and shared nothing was a good design in general, I think that vectors are special. They are slow, a lot slower than other data structures modeled by Redis. As a bonus point, a

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ytes in Java, and the agent has the correct idea of converting each number into ASCII, but it incorrectly assumes that 063 and 070 are decimals and copies them as such. After encouraging the agent to consider different possible encodings, it correctly copies those numbers as octal and solves task. View fail in Docent | View success in Docent The task presents an RSA decryption challenge in which the agent is given eee, NNN, and C:=Memod NC := M^e \mod NC:=MemodN, and it must find MMM. The prompt

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bandwidth and compute cycles The Solution: Fusion The fused approach combines matrix multiplication and scaling into a single hardware operation: Why This Matters This fusion leverages AMD GPUs' specialized hardware (particularly on MI300X with native FP8 support): Memory Efficiency: Scaling happens within on-chip memory before writing results; Computational Efficiency: Eliminates redundant operations; Performance Boost: Our tests show up to 2.5× speedup compared to the naive implementation The

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starting from scratch implementation focused on iterating each building block with the feedback and contributions from the community; Production stack's desired strength is to leverage built-in KV cache-focused optimizations (transfer, blending, routing), especially beneficial in long-context and prefill-heavy workloads. In the near term, production stack plans to leverage components from AIBrix Is AIBrix a community driven project? Absolutely. The purpose of open-sourcing it under vLLM project

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r, it still shares a lot of code with vLLM V0, such as model implementations, GPU kernels, distributed control plane, and various utility functions. This approach allows V1 to leverage the extensive coverage and stability established by V0 while delivering significant enhancements to performance and code complexity. 1. Optimized Execution Loop & API Server As a full-fledged continuous batching engine and OpenAI-compatible API server, vLLM’s core execution loop relies on CPU operations to manage

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ck built on top of vLLM, designed to run seamlessly on a cluster of GPU nodes. It adds four critical functionalities that complement vLLM’s native strengths: KV cache sharing & storage to speed up inference when context is reused (powered by the LMCache project); Prefix-aware routing that sends queries to the vLLM instance already holding the relevant context KV cache; Observability of individual engine status and query-level metrics (TTFT, TBT, throughput); Autoscaling to handle dynamics of wor

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6, 2024 Llama 3.2 Vision is now available to run in Ollama, in both 11B and 90B sizes. Download Ollama 0.4, then run: To run the larger 90B model: To add an image to the prompt, drag and drop it into the terminal, or add a path to the image to the prompt on Linux. Note: Llama 3.2 Vision 11B requires least 8GB of VRAM, and the 90B model requires at least 64 GB of VRAM. Examples Handwriting Optical Character Recognition (OCR) To use Llama 3.2 Vision with the Ollama Python library: JavaScript Libr

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and coding domains; Developing a marketplace that allows anyone to contribute their own compute resources; Creating a platform that enables anyone to initiate a training run, leveraging distributed compute ‍ Join Us in Building the Open Future of AI Open-source AI is the key to countering the risks of centralization, but it requires coordinated efforts to harness distributed compute, talent, and capital to compete with leading closed-source labs. Join us to advance open and democratized AI: If

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efault. Dataset. We benchmark different serving engines using the following three datasets: ShareGPT: 500 prompts randomly sampled from ShareGPT dataset with fixed random seed. Average input tokens: 202, average output tokens: 179; Prefill-heavy dataset: 500 prompts synthetically generated from sonnet dataset with roughly 462 input tokens and 16 output tokens on average; Decode-heavy dataset: 500 prompts synthetically generated from sonnet dataset with roughly the same amount of 462 input tokens

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e to be consumed as APIs. These include models for translation, speech recognition, speech synthesis, & document parsing. Sarvam announced their API platform for developers to leverage these models for building their GenAI use cases. A1: A generative AI workbench designed for modern lawyers' to enhance their capabilities with features such as regulatory chat, document drafting, redaction and data extraction "Our mission is to democratize AI and make it accessible to every Indian, regardless of t

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print, increases throughput, lowers latency, and comes with minimal accuracy drops. Currently, vLLM supports the official Meta Llama 3.1 405B FP8 model quantized via FBGEMM by leveraging per-channel quantization in the MLP layer. In particular, each channel of the up/gate/down projections are quantized and multiplied by a static scaling factor. Combined with skipping quantization for the first and the last layer, and a static upper bound, this approach has minimal impact on the model’s accuracy.

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and the gradients are all-reduced in FP16. Due to the large number of local steps, the four workers run independently for around 67.5 minutes before communicating for gradient averaging. For the outer optimizer step, our experiment shows an average all-reduce time between the workers of 300 seconds. Due to DiLoCo’s significant reduction in communication time, the all-reduce bottleneck only accounts for 6.9% of the training time, minimally impacting the overall training speed. Additional trainin

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cations will expand exponentially, unlocking new use cases from everyday tasks to scientific research. Despite an explosion in demand, the AI compute market remains primitive and fragmented. NVIDIA's incentives contribute to this fragmentation, while providers like AMD, Groq and others further broaden the landscape to pick choose from with specialized chips. The market still lacks the efficiency seen in exchanges. Fragmented Pricing: H100 GPU costs fluctuate wildly from 1.2to1.2 to 1.2to18 per h

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