All NVIDIA items under Companies on AIFIRST News, newest first. 7 items.
Nvidia has begun notifying customers of AI-related price increases exceeding 15% across its product lines. The hikes are linked to sustained demand for AI accelerators and constrained supply. The price increases affect both consumer and enterprise hardware buyers in the local AI ecosystem.
A developer hosted Kimi K3, Moonshot AI's 2.8 trillion parameter model, on 8 NVIDIA B300 GPUs via Modal, achieving 92 tokens per second at $190 per million tokens. Cold boot takes around 27 minutes to load the 1.56 TB model at a cost of roughly $56.79 per hour. The experiment shows frontier-scale inference is now accessible but remains expensive outside managed cloud services.
A technical comparison analyzes Etched's Sohu transformer ASIC against Nvidia GPUs for inference workloads. The Sohu chip is purpose-built for transformer architectures, offering high throughput per watt but less flexibility than general-purpose GPUs. The analysis covers cost-per-token, latency, and deployment trade-offs for production inference at scale.
energygraph v1.3 adds discrete GPU power monitoring for NVIDIA, Intel, and AMD cards, extending the tool beyond its previous CPU-only scope. The tool is open-source with zero build dependencies and displays live power consumption data directly in the terminal.
Fizgig v4.3.0 adds AMD Radeon support via ROCm, covering RDNA1 through RDNA4 GPUs, for LoRA training on Flux 2 Klein, Krea 2, and MiniMax H3. Previously NVIDIA-only, the free open-source tool now provides an AMD-native training path on Windows.
Nari Labs describes five inference optimizations applied to Qwen3-TTS on a single NVIDIA H100 SXM, reaching 10 requests per second and sub-50 ms p95 time to first audio. Throughput is about 630 characters per second at full load, at roughly $2 per 1 million characters. The post benchmarks the result against vLLM-Omni, SGLang-Omni, and VoxServe, and against the commercial services ElevenLabs and Cartesia, claiming lower latency at higher request volumes.
llama.cpp is a C/C++ implementation of large language model and vision language model inference, built on the ggml tensor library and MIT licensed. It ships a command line interface, an OpenAI-compatible API server, and a web chat UI, installable as an app, via Docker, from pre-built binaries, or from source. Backends cover Apple Silicon with ARM NEON, Accelerate and Metal, x86 with AVX through AVX512 and AMX, NVIDIA CUDA, AMD HIP, Vulkan and SYCL, plus hybrid CPU and GPU inference. The repository showed 124.9k stars and 22.0k forks with 778 open issues and roughly 1.4k open pull requests, and quantization spans 1.5-bit to 8-bit so output quality depends on the level chosen.
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