Models & Labs
Together AI Launches Provisioned Throughput for Open Models
Together AI Bloghigh confidence
Why it matters
- →Provides a cost-effective alternative to proprietary models, reducing inference costs by up to 90%.
- →Offers predictable pricing and guaranteed capacity, crucial for production workloads.
- →Facilitates the adoption of open models in enterprise environments, enhancing scalability and reliability.
Together AI has introduced Provisioned Throughput, a new service offering reserved inference capacity for open models with token-based pricing and a 99% uptime SLA. This service aims to provide companies with a cost-effective and reliable alternative to proprietary models, offering up to 90% savings compared to models like Claude Opus 4.8. Provisioned Throughput is available for models such as MiniMax M3 and GLM-5.2, with capacity in North America and EMEA. This development is expected to facilitate the adoption of open models in production environments by providing predictable pricing and guaranteed capacity.
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Llama.cpp b9946 Release Enhances Hexagon Optimizations
The latest b9946 release of llama.cpp focuses on optimizing Hexagon operations, particularly unary operations, to improve performance and efficiency. By introducing tiling for wide rows and replacing divisions with fastdiv, the update aims to prevent VTCM overflow and streamline code execution. The release also includes tracing instrumentation and specialized thread functions to enhance code generation. While no new models are introduced, these technical improvements make llama.cpp more robust and efficient for developers working with Hexagon architectures.
llama.cpp Releases
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Llama.cpp b9948 Release Enhances CUDA Efficiency
The latest b9948 release of llama.cpp focuses on optimizing memory usage in CUDA operations, specifically in the ggml_top_k() and ggml_argsort() functions. By processing data in smaller chunks, the update reduces the need for large temporary buffers, enhancing performance on CUDA-enabled systems. This release also includes minor code improvements like allocating temporary destinations only once and refining the use of ternary operators. While no new model architectures are introduced, these changes make llama.cpp more efficient for developers working with CUDA, particularly in memory-constrained environments.
llama.cpp Releases
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llama.cpp b9951 Release Enhances ET Backend
The latest b9951 release of llama.cpp marks a significant enhancement in the ET backend, introducing a range of new kernels and performance optimizations. This update includes the addition of various matrix operations and support for FlashAttention, which promises to improve computational efficiency. The release also focuses on vectorization and parallelization, aiming to boost performance across different operations. These changes make the ET backend more robust and capable, potentially benefiting developers working with complex AI models by offering improved speed and functionality.
llama.cpp Releases