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⚡ Continuous Batching
GitHub
·
2d
2 days ago
I got tired of not understanding how
vLLM
works under the hood, so I built my own mini
inference
engine from scratch.
Discussed on
r/LLM
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Actions for I got tired of not understanding how vLLM works under the hood, so I built my own mini inference engine from scratch.
thecomputersciencebook.com
·
6d
6 days ago
PagedAttention
is more than virtual memory
Covers
Efficient Memory Management for Large Language Model Serving with PagedAttention
Discussed on
Hacker News
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Actions for PagedAttention is more than virtual memory
portal.neuralwatt.com
·
12h
12 hours ago
Neuralwatt: Energy-based pricing for AI
inference
. Efficient prompts cost less
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Hacker News
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Actions for Neuralwatt: Energy-based pricing for AI inference. Efficient prompts cost less
ubuntu.com
·
19h
19 hours ago
Developing web apps with local
LLM
inference
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Actions for Developing web apps with local LLM inference
fitservers.com
·
2d
2 days ago
The Complete Guide to Deploying DeepSeek R1 on a Dedicated Server
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Actions for The Complete Guide to Deploying DeepSeek R1 on a Dedicated Server
thecybersidekick.beehiiv.com
·
3d
3 days ago
AI
Inference
at the Edge: Running Real-Time LLMs in Kubernetes Without a GPU Farm
Discussed on
DEV
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Actions for AI Inference at the Edge: Running Real-Time LLMs in Kubernetes Without a GPU Farm
digitalocean.com
·
2d
2 days ago
Efficient
LLM
Compression with SparseGPT and Wanda on GPU Cloud
Covers
NVIDIA Triton Inference Server — NVIDIA Triton Inference Server
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Actions for Efficient LLM Compression with SparseGPT and Wanda on GPU Cloud
vettedconsumer.com
·
6d
6 days ago
The
KV
Cache
, Explained: Why Long Context Eats Your VRAM (and How to Fit More)
Covers
2 stories
See all stories this covers
including
Efficient Memory Management for Large Language Model Serving with PagedAttention
Discussed on
Hacker News
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Actions for The KV Cache, Explained: Why Long Context Eats Your VRAM (and How to Fit More)
medium.com
·
2d
2 days ago
vLLM
, Function Calling, and World Models explained
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Actions for vLLM, Function Calling, and World Models explained
arxiv.org
·
6d
6 days ago
SwiftCache: Efficient
LLM
Serving for Multi-turn Conversations with Heterogeneous
KV
Cache
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Actions for SwiftCache: Efficient LLM Serving for Multi-turn Conversations with Heterogeneous KV Cache Sharing
medium.com
·
1d
1 day ago
The Context Budget That Will Decide Everyday AI
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Actions for The Context Budget That Will Decide Everyday AI
Anyscale blog posts
·
3d
3 days ago
High Performance Distributed
Inference
with Ray Serve
LLM
Covered by
Google Cloud Blog
Discussed on
Hacker News
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Actions for High Performance Distributed Inference with Ray Serve LLM
pyimagesearch.com
·
6d
6 days ago
RAG Observability with Langfuse,
vLLM
, and FAISS
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Actions for RAG Observability with Langfuse, vLLM, and FAISS
networkworld.com
·
4d
4 days ago
Tether is shipping TurboQuant
KV-cache
quantization with Vulkan support into its QVAC SDK
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Actions for Tether is shipping TurboQuant KV-cache quantization with Vulkan support into its QVAC SDK
latent.space
·
2d
2 days ago
[AINews] GLM > GPT? GLM-5.2 passes vibe check; Z.ai forecasts Open Fable by December
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Actions for [AINews] GLM > GPT? GLM-5.2 passes vibe check; Z.ai forecasts Open Fable by December
lector.dev
·
2d
2 days ago
Show HN: Evaluating Local LLMs as language translators for my app
Discussed on
Hacker News
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Actions for Show HN: Evaluating Local LLMs as language translators for my app
Google Cloud Blog
·
4d
4 days ago
Scaling Ray Serve
LLM
on GKE: Performance without losing the developer experience
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Actions for Scaling Ray Serve LLM on GKE: Performance without losing the developer experience
mstar.stanford.edu
·
3d
3 days ago
M* (M-Star): A Modular, Extensible, Serving System for Multimodal Models
Discussed on
Hacker News
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Actions for M* (M-Star): A Modular, Extensible, Serving System for Multimodal Models
devashish.me
·
5d
5 days ago
Two Qwen3 models on one DGX Spark: the residency math
Discussed on
Hacker News
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Actions for Two Qwen3 models on one DGX Spark: the residency math
lemmy.world
·
1d
1 day ago
Hivekeep - self-host a team of AI agents in one container, with a real UI (MIT)
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Actions for Hivekeep - self-host a team of AI agents in one container, with a real UI (MIT)
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