a66996ac10
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.
2.3 KiB
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
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:
- Start with vLLM.
- Test six simultaneous users.
- Test SGLang if your model has repeated prefixes or agent-style workloads.
- 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.