The efficiency race in LLMs just got louder. 65% VRAM cuts during fine-tuning without quantization? Check peftee. Training-free diffusion acceleration? Yes — Fast-DLLM. And a pluggable speed boost for inference? Enter ChunkLLM. [1][2][3]
peftee sticks to memory reduction without sacrificing precision, offering a notable VRAM drop during fine-tuning without resorting to quantization. [1]
Fast-DLLM targets training-free acceleration for diffusion LLMs, promising speedups without extra training. [2]
ChunkLLM provides a lightweight, pluggable framework to accelerate LLM inference, aiming to slot into existing stacks with minimal fuss. [3]
Larger-model realities are surfacing too. A thread on Strix Halo and LM Studio flags memory and context-size headaches on high-RAM setups, with discussions about GTT pools and BIOS tweaks shaping how much VRAM is effectively usable. [4]
On hardware front lines, DGX Spark vs RTX 3090 benchmarks show a mixed bag: the 3090 often outpaces Spark for smaller models, while Spark still crushes with very large VRAM footprints. Bigger models tilt toward distributed, enterprise-scale layouts. [5]
The trend isn’t one trick — it’s a blend of memory-management hacks, fast DLLM tricks, and modular inference accelerators. Expect more open frameworks and cross-vendor optimization as models keep growing.
References
Show HN: Efficient LLM fine-tuning with 65% less VRAM without quantization
Show HN post on efficient LLM fine-tuning using 65% less VRAM without quantization; GitHub repo linked
View sourceFast-DLLM: Training-Free Acceleration of Diffusion LLM
Paper proposes training-free DLLM acceleration for diffusion-based LLMs, focusing on faster generation and efficiency improvements.
View sourceChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference
Presents ChunkLLM, a lightweight pluggable framework to speed up large language model inference, with design goals and integration considerations aspects.
View sourceStrix Halo and LM Studio Larger Model Issues
Discusses larger LLMs, context limits, VRAM allocation, quantization, model comparisons (GLM, Cerebras), kernels and BIOS tweaks; Linux setups influence results.
View sourceBenchmarking the DGX Spark against the RTX 3090
Ollama benchmarks DGX Spark vs RTX 3090 for inference speeds; compares small and large models; questions value and use cases.
View source