I took the liberty of adding your reasoning effort chat template to my setup. You can play around with the last few parameters. In generall VLLM will be better in higher concurrency scenarios, so if you only use it for a personal vibe coding assistant and less as a general home model for task execution llama.cpp may be better.
yes, you know, personal use doesn't necessary mean no concurrency.
it's good to play with harness setup where you fan out multiple concurrent branches that share non trivial amount of prefix then reduce their output/summary back into main agent.
ie. instead of serially reading further skills/relevant source files for planning/thinking, you can branch and read them in parallel reusing prefix / or use to to approach request from different angles in parallel - to map-reduce result onto main context of what's actually relevant. branching subagents has benefits of not polluting main context, shared prefix prefill is close to free on a cache hit and with concurrent decoding/continuous batching you can utilize gpu well to get good speedups.
ie. what's relevant is number of active concurrent sequences (and their shape, ie. shared prefix), not so much number of users.
i'm not sure with llama.cpp vs vllm regarding concurrency – llama server has multiple server slots, continuous/dynamic batching enabled by default, prompt caching (also on by default), ram prompt cache, context checkpoints, unified kv buffer across sequences etc. so shouldn't be bad, i guess would be good to actually benchmark. personally i'm happy with llama.cpp.
Here's a VLLM command for 3.6 (I'll update to 3.8 today) to test out:
```
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
vllm serve Qwen/Qwen3.6-27B-FP8 \
--dtype auto \
--kv-cache-dtype fp8 \
--enable-chunked-prefill \
--enable-prefix-caching \
--trust-remote-code \
--enable-auto-tool-choice \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder \
--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":3}' \
--default-chat-template-kwargs '{
```I took the liberty of adding your reasoning effort chat template to my setup. You can play around with the last few parameters. In generall VLLM will be better in higher concurrency scenarios, so if you only use it for a personal vibe coding assistant and less as a general home model for task execution llama.cpp may be better.