Orion vault — clean initial history

Knowledge vault (Orion/PARA) migrated from the pre-Orion 484vault on
2026-10-01. Deliberately orphaned: prior history contained a plaintext
password and stays local-only on branch archive/pre-boilerplate-history.
Secrets and live Hermes state are gitignored.

2. Areas/Home Personal/knowledge/H200 VRAM Efficiency.md

@@ -0,0 +1,41 @@
+
+ is there somethig more efficient than lama.cpp
+
+Yes. With an H200, `llama.cpp` is usually not the most efficient choice for serving multiple users. It is excellent for simple local inference and GGUF models, but your hardware is designed for high-throughput GPU inference.
+
+### Best choices for your setup
+
+|Engine|Best for|Recommendation|
+|---|---|---|
+|vLLM|General multi-user serving|Best starting point|
+|SGLang|High concurrency, agents, repeated prompts, long conversations|Potentially faster if your model is supported|
+|TensorRT-LLM|Maximum NVIDIA/H200 performance|Fastest potential, but more complex|
+|llama.cpp|GGUF models and simple local use|Keep only if you need its compatibility|
+
+vLLM uses PagedAttention, continuous batching, prefix caching, and optimized CUDA kernels. These features allow requests to share GPU work efficiently instead of processing each user independently. [vllm.ai](https://docs.vllm.ai/)
+
+TensorRT-LLM is the NVIDIA-specific option. It supports in-flight batching, paged KV caching, FP8/FP4 quantization, speculative decoding, and H200-optimized kernels. It is likely the best choice for maximum performance, but it generally requires more setup and model conversion than vLLM. nvidia.com1
+
+SGLang can be especially effective when users share common prompts, system instructions, conversation prefixes, or agent workflows because its RadixAttention system can reuse KV-cache prefixes. arXiv2
+
+My recommendation would be:
+
+1. Start with vLLM.
+2. Test six simultaneous users.
+3. Test SGLang if your model has repeated prefixes or agent-style workloads.
+4. Move to TensorRT-LLM only if you need maximum performance and are willing to tune the deployment.
+
+A basic vLLM launch might look like:
+
+bash
+
+```
+vllm serve /path/to/model \  --host 0.0.0.0 \  --port 8000 \  --max-model-len 8192 \  --max-num-seqs 6 \  --gpu-memory-utilization 0.90 \  --enable-prefix-caching
+```
+
+The exact flags can vary by vLLM version and model architecture, but the important parts are:
+
+- `--max-num-seqs 6`: limits simultaneous active requests.
+- `--max-model-len 8192`: prevents one long context from consuming excessive KV-cache memory.
+- `--gpu-memory-utilization 0.90`: leaves some VRAM for CUDA and server overhead.
+- `--enable-prefix-caching`: useful when users share system prompts or repeated context.