With 33–48 GB,
here is what really runs.
Experiences reported by people using their machines every day. The figure is the reported setup capacity; multi-GPU and unified-memory layouts can behave differently.
in this range
Qwen 27B coding and infrastructure on two 3090s
A two-3090 local setup runs Qwen 27B for coding and infrastructure work, including bug fixing and quality-of-life automation.
27B reasoning model on 2x RTX 2080 Ti (NVFP4, vLLM) for agentic orchestration
Qwen3.8-27B (NVFP4 W4A16 + FP8 KV) served on 2x RTX 2080 Ti 22GB over NVLink via a vLLM TP2 fork. 196K context cap, MTP K=3, prefix caching on. Feeds a 1-orchestrator + 7-worker harness: ~1227 tok/s prefill and ~29 tok/s decode solo at 65K, ~192 tok/s aggregate at 8-way. Rejected AWQ/FP8 weights (72% EOS failure), ExLlamaV3 (3-6x slower), and PP=2 (infeasible on 2 GPUs).
Benchmarking Strata n-gram table placement on a modded RTX 4090 48GB with 128GB DDR5
A measured benchmark on a modded 48GB RTX 4090 shows Strata's n-gram table belongs on SSD while conversation parking delivers the large speedup.
Compare with another budget.
Every capacity guide, or start from your own machine and a task.