How Do You Actually Scale High-Throughput LLM Serving in Production with vLLM?
Break the VRAM wall. Master PagedAttention, dynamic quantization, and memory-efficient orchestration for enterprise AI. Continue reading on Medium »
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The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading
arXiv:2604.03501v1 Announce Type: new Abstract: Experimental evidence confirms that AI tools raise worker productivity, but also that sustained use can erode the expertise on which those gains depend. We develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time, trading immediate productivity against the erosion of worker skill. We decompose the tool's productivity effect into two channels, one independent of worker expertise and one that scales with it. The model produces three main results. First, even a decision-maker who fully anticipates skill erosion rationally adopts AI when front-loaded productivity gains outweigh long-run skill costs, producing steady-state loss: the worker ends up less productive than before adoption. Second, when manager
Tested TurboQuant KV compression with Gemma 4 31B — 5.80x compression, perfect long-context recall, JSON output preserved
Quick experiment: I implemented Google Research's TurboQuant paper (arXiv 2504.19874) as a Python package and tested it with Google's brand new Gemma 4 31B model. The results exceeded the paper's claims. Setup: Hardware: RTX PRO 6000 Blackwell (96GB VRAM) Model: google/gemma-4-31B-it (BF16, 64 layers) Compression: TurboQuant turbo3 (3-bit PolarQuant + QJL residual) Backend: HuggingFace Transformers with custom past_key_values interception Compression results: Metric Value FP16 baseline (222 tokens) 218.23 MB TurboQuant compressed 37.62 MB Compression ratio 5.80x Paper theoretical (turbo3) 4.9x The 5.80x exceeds the paper's 4.9x claim — likely because Gemma 4's architecture has fewer outliers in the K/V distributions than the synthetic data used in the paper. Functional validation: Multi-tu
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![trunk/06cee8b2f9c6b2c10076efb3082adb7c2605a98c: [vllm hash update] update the pinned vllm hash (#179531)](https://avatars.githubusercontent.com/u/133916390?s=60&v=4)
Tested TurboQuant KV compression with Gemma 4 31B — 5.80x compression, perfect long-context recall, JSON output preserved
Quick experiment: I implemented Google Research's TurboQuant paper (arXiv 2504.19874) as a Python package and tested it with Google's brand new Gemma 4 31B model. The results exceeded the paper's claims. Setup: Hardware: RTX PRO 6000 Blackwell (96GB VRAM) Model: google/gemma-4-31B-it (BF16, 64 layers) Compression: TurboQuant turbo3 (3-bit PolarQuant + QJL residual) Backend: HuggingFace Transformers with custom past_key_values interception Compression results: Metric Value FP16 baseline (222 tokens) 218.23 MB TurboQuant compressed 37.62 MB Compression ratio 5.80x Paper theoretical (turbo3) 4.9x The 5.80x exceeds the paper's 4.9x claim — likely because Gemma 4's architecture has fewer outliers in the K/V distributions than the synthetic data used in the paper. Functional validation: Multi-tu
Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
arXiv:2604.03523v1 Announce Type: new Abstract: Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high collection cost. Furthermore, imitation learning algorithms assume that the data is independently and identically distributed, which ultimately results in poorer performance as gradual errors emerge and compound within test-time trajectories. We address these issues by introducing the "master your own expertise" (MYOE) framework, a self-imitation framework that enables robotic agents to learn complex behaviors from limited demonstration data samples. Inspired by human perception and action, we propose and design what we call the queryable mixture-of-preferences state sp
langchain-ollama==1.1.0
Changes since langchain-ollama==1.0.1 release(ollama): 1.1.0 ( #36574 ) feat(ollama): support response_format ( #34612 ) fix(ollama): serialize reasoning_content back to ollama thinking ( #36573 ) fix(ollama): prevent _convert_messages_to_ollama_messages from mutating caller list ( #36567 ) feat(ollama): add dimensions to OllamaEmbeddings ( #36543 ) fix(ollama): respect scheme-less base_url ( #34042 ) feat(ollama): logprobs support in Ollama ( #34218 ) chore(ollama): switch to ty ( #36571 ) chore: add comment explaining pygments>=2.20.0 ( #36570 ) chore: pygments>=2.20.0 across all packages ( CVE-2026-4539 ) ( #36385 ) chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/ollama ( #36249 ) chore(partners): bump langchain-core min to 1.2.21 ( #36183 ) ci: suppress pytest streaming ou
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