A detailed r/LocalLLaMA benchmark reports single- and dual-GPU numbers for Qwen3.5-27B int4 on Intel Arc Pro B70 32GB using Intel’s vLLM fork. The setup is still finicky, but the measurements outline a practical path for local serving on Intel hardware.
vLLM said NVIDIA used the framework for the first MLPerf vision-language benchmark submission built on Qwen3-VL. NVIDIA’s accompanying blog places that result inside a broader Blackwell Ultra push that claims up to 2.7x throughput gains and more than 60% lower token cost on the same infrastructure for some workloads.
A popular r/LocalLLaMA self-post lays out a concrete 2x H200 serving stack for GPT-OSS-120B, including routing, monitoring, and queueing tradeoffs. The appeal is not just the headline throughput, but the unusually detailed operational data behind it.
A LocalLLaMA thread pulled attention to DFlash, a block-diffusion draft model for speculative decoding whose paper claims lossless acceleration above 6x and direct support for vLLM, SGLang, and selected Transformers backends.
A March 26, 2026 r/LocalLLaMA post about serving Qwen 3.5 27B on Google Cloud B200 clusters reached 205 points and 52 comments at crawl time. The linked write-up reports 1,103,941 total tokens per second on 12 nodes after switching from tensor to data parallelism, shrinking context length, enabling FP8 KV cache, and using MTP-1 speculative decoding.
A March 12, 2026 LocalLLaMA benchmark post claims the best sustained decode for Qwen3.5-397B NVFP4 on 4x RTX PRO 6000 Blackwell GPUs is 50.5 tok/s with Marlin, because native CUTLASS grouped GEMM paths on SM120 fail or fall back.
A March 14, 2026 LocalLLaMA post outlined a CUTLASS and FlashInfer patch for SM120 Blackwell workstations, claiming major gains for Qwen3.5-397B NVFP4 inference and linking the work to FlashInfer PR #2786.
A well-received PSA on r/LocalLLaMA argues that convenience layers such as Ollama and LM Studio can change model behavior enough to distort evaluation. The more durable lesson from the thread is reproducibility: hold templates, stop tokens, sampling, runtime versions, and quantization constant before judging a model.
A LocalLLaMA post reports that a simple “verify after every edit” loop raised Qwen3.5-35B-A3B from 22.2% to 37.8% on SWE-bench Verified Hard, approaching a cited 40% reference for Claude Opus 4.6.
A community developer achieved 100+ t/s decode speed and 585 t/s aggregate throughput for 8 simultaneous requests running Qwen3.5 27B on a dual RTX 3090 setup with NVLink, using vLLM with tensor parallelism and MTP optimization.