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GPU memory planning starts with one equation: required VRAM = model weights + KV cache + runtime reserve. A checkpoint that fits by file size can still fail when several requests hold long contexts, while a model that loads successfully can still miss its latency target. The ...
Sticky sessions are not a universal MCP requirement anymore. A remote Model Context Protocol client speaking 2026-07-28 sends self-describing requests that may reach any compatible replica, while a 2025-era Streamable HTTP client can still depend on Mcp-Session-Id and server-local session state. Removing affinity before identifying which contract ...
nvidia-smi can show a healthy GPU while an Ollama request still runs partly—or entirely—on CPU. That contradiction does not have one universal cause. The loaded model's PROCESSOR field is the starting evidence: it separates full GPU placement, full CPU placement and a mixed split before you touch ...
Quick answer: VPS specs for Ollama depend on model size, quantization, context length, concurrency, and whether the workload is CPU-only or GPU-backed. For small local LLM testing, 4 vCPU, 8 GB RAM, and NVMe storage can be enough. For practical 7B to 8B use, 8 vCPU and ...
Quick answer: Choose GPU infrastructure by workload pattern. Cloud GPU works well for short experiments and bursty training. Dedicated GPU servers fit sustained inference, rendering, private data, and predictable usage. Hybrid GPU setups combine a private control plane with temporary external GPU capacity. A standard VPS can ...
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