Local deployment of larger open models is forcing a rethink of GPU memory, RAM sizing, and quantization. Rumors of high-VRAM RTX 5090 cards and tests showing KV cache costs comparable to weights highlight the new hardware bottleneck.
Inkling's second generation debuts with a 594 billion total parameter MoE and 17 billion active parameters, image inputs, and an Apache 2.0 weight release after safety testing. The model improves scores on the Artificial Analysis Intelligence Index. ...
The November 10 deadline for PyTorch Day China 2026 reveals an agenda focused on distributed training, efficient inference, hardware acceleration, and physical AI. The analysis: the chosen tracks show the framework increasingly looking at production ...
ricursiveAI/Ripple-65B-Preview appears on Hugging Face with no technical details. For on-premise LLM evaluation, the 65B class forces VRAM planning and quantization. The Preview label shifts validation costs to local teams, making the release a test ...
The PyTorch Day China 2026 Call for Proposals closes November 10. The December 12 event in Beijing highlights five topic areas, with a notable emphasis on efficient inference, hardware acceleration, and physical AI. For teams evaluating local stacks ...
The rumor about GeForce RTX 5090 with 64 and 96 GB of VRAM hits the critical bottleneck for local LLM users: video memory. An analysis of implications for self-hosted deployments, quantization, TCO, and data sovereignty, beyond the hardware announcem...
A US GPU repair technician has reportedly claimed the existence of RTX 5090 cards with 64 and 96 GB of VRAM. The unverified rumor touches the core constraint for local inference: more video memory enables larger models, less aggressive quantization, ...
A 9-billion-parameter preview on Hugging Face highlights how vendors use early releases to test demand for self-hosted LLMs. With no benchmarks or hardware details, the value lies in validating pipelines and VRAM usage before on-premise decisions.
A Mac M4 Max test with a Qwen3.8 27B model and 164k tokens of context shows cache and KV can cost as much as weights. The old 64GB rule no longer holds.
