Quantization formats, speculative decoding, and token-reduction methods are improving throughput and reducing hardware requirements for local inference.
Specter combines quantization, speculative decoding, and expert offloading to speed up mixture-of-experts inference on a single GPU. A lightweight quantized draft model runs on-device and anticipates expert usage with high fidelity, reducing PCIe bot...
The new EAGLE-4 method posts a 3.59× speedup over vanilla decoding in published benchmarks, ahead of DFlash2 (2.80×), DSpark (2.57×) and MTP (2.22×). Support is currently limited to Qwen3.8-27B, and integration with llama.cpp and vLLM is missing. For...
Migtissera has released a GGUF fine-tune of a Qwen model. Internal benchmarks suggest it produces up to a quarter fewer tokens for the same score. Fewer tokens mean faster responses, longer sessions and lower inference costs—an advantage for teams ru...
ISTA-DASLab updated the Qwen3.8-Flash-Next-GSQ-RCO-Coder GGUF model. Retained weights are unchanged from the IQ3_S base, but about 25% of the retained expert selection differs. The model remains English-only by design: with a limited expert budget, E...
A proposal for llama.cpp introduces IQ2_K–IQ6_K types: same file sizes as Q-quants, substantially better KL divergence, slightly lower performance and slower creation. For local LLM deployment, the trade-off shifts: same VRAM footprint, output closer...
A Reddit experiment shows that replacing the heavily quantized PLE in common GGUFs with a full FP8 sidecar improves the perceived quality of Qwen 3.8 Flash Next, with no speed impact. Only disk usage increases.
The ORL (On-Policy Residual Learning) project aims to speed up diffusion LLMs without sacrificing quality. On a single RTX 5090 the team reports 2,788 tokens/s with LLaDA-2.1-mini in NVFP4; the open-source dlmrt inference engine uses CUDA graphs and ...
A preprint from Stanford, Together AI, UIUC, and Meta introduces Hybrid Reasoning Agents: models that reason internally and emit text only for tool use and final answers. In reported tests they gain about 10%, use half the tokens, run twice as fast, ...
A three-day empirical test of GYRO-M, a Q2 quantization of Qwen Flash Next, reports q3-level stability at reduced size. The project comes from a single contributor and holds up on long contexts, but shows weaknesses on Cyrillic text. The case reopens...
