Two in the morning, eighteen hours into the run. Seven LLM backends processing the same stream of GPU cluster anomalies. Same thermal cascades, same NVLink errors, same KV cache evictions. I’m watching the scoring dashboard update in real time and the numbers are breaking my assumptions faster than I can take notes. The $32-per-day model is getting the diagnosis wrong more often than a free one running on my workstation. ...
Part 2 ended with a promise: find the cliff. Run the MoE model from four concurrent agents upward until the physics says stop. We scaled to eight. The cliff never came. This is Part 3 of the Local LLM Bench series. Part 1 covers the single-request baseline. Part 2 established the MoE advantage under concurrent load. The model: Qwen3-Coder-30B-A3B — a Mixture-of-Experts architecture that activates only 3.3B of its 30B parameters per token. On consumer GPUs, that sparse activation leaves ~90% of memory bandwidth idle at batch size 1, creating headroom that concurrent agents fill. Dense models activate all 32B parameters on every token — already at the bandwidth ceiling before the second agent connects. Part 1 explains why these specific models were chosen (best in class for each architecture); Part 2 conclusively eliminated Dense under concurrent load. This benchmark tests MoE only. ...
In Part 1, we established the baseline: MoE delivers 168 tok/s on a single RTX 3090, 4.1x faster than Dense. Clean single-request numbers. One prompt in, one response out. That’s not how swarms work. An orchestrator like Claude Code dispatches four coding tasks simultaneously. The local model serves all four. Under concurrency, memory bandwidth saturates, per-task throughput drops, and the architecture of the model — not the GPU, the model — determines whether you get useful parallelism or just contention. ...
I went looking for sustained-load benchmarks comparing MoE and Dense coding models on consumer GPUs. Not demo bursts on a Mac Mini — sustained autoregressive generation on real coding tasks, where architecture and interconnect are the only variables. I found plenty of one-shot numbers. Nobody had published the comparison that matters: same hardware, same quantization, same inference engine, MoE versus Dense, across GPU configurations. Methodology visible. Numbers verifiable. So I ran the tests. Dual RTX 3090s with NVLink, custom liquid cooling, a 6 kW isolation transformer feeding a double-conversion UPS. Not elegant, but thermally and electrically honest — sustained inference loads without throttling, no measurement fiction. The hardware details are below. ...
I’m currently working on the design of a framework for GPU fleet management. We’re living in a crowded data center reality where everybody wants “hero” compute — dense GPUs, fast networking, and delivery that’s closer to the edge. We’re in a land-grab phase where every business wants to be everywhere, but most teams are discovering the same thing: buying GPUs is the easy part. Operating them as a coherent fleet is the hard part. ...