Large-scale LLM inference can be extremely expensive. What strategies would you use to optimize cost while maintaining acceptable performance?
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
November 3, 2026 at 6:55 AM
Large-scale LLM inference can be extremely expensive. What strategies would you use to optimize cost while maintaining acceptable performance?
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
vLLM Surges as Open-Source Engine for High-Speed LLM Serving
#vLLM #LLMinference #opensource
https://singulism.com/en/2026-10-22-vllm-llm-serving-overview/
#vLLM #LLMinference #opensource
https://singulism.com/en/2026-10-22-vllm-llm-serving-overview/
vLLM Surges as Open-Source Engine for High-Speed LLM Serving
UC Berkeley-born vLLM surges as an LLM inference platform, achieving high throughput with PagedAttention and continuous batching.
singulism.com
November 1, 2026 at 6:41 PM
vLLM Surges as Open-Source Engine for High-Speed LLM Serving
#vLLM #LLMinference #opensource
https://singulism.com/en/2026-10-22-vllm-llm-serving-overview/
#vLLM #LLMinference #opensource
https://singulism.com/en/2026-10-22-vllm-llm-serving-overview/
What optimizations would you employ to achieve high throughput and low latency?
How would you answer this? I'm curious which trade-off you would tackle first in a production setting.
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
How would you answer this? I'm curious which trade-off you would tackle first in a production setting.
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
November 4, 2026 at 6:55 AM
What optimizations would you employ to achieve high throughput and low latency?
How would you answer this? I'm curious which trade-off you would tackle first in a production setting.
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
How would you answer this? I'm curious which trade-off you would tackle first in a production setting.
Full post · Follow me: LinkedIn · GitHub · Bluesky · X · Dev.to · Facebook
#AIInterview #LLMInference #GenAI
KVCache Meetup Korea의 후기가 @pytorch.org Foundation 공식 블로그에 게재되었습니다! 🎉
한국어 후기는 @pytorch.kr 블로그에서, 공개된 행사 영상은 PyTorchKR YouTube에서 보실 수 있습니다:
* 한국어 블로그: pytorch.kr/blog/2026/kv...
* KVCache Meetup 영상: www.youtube.com/playlist?lis...
#PyTorch #PyTorchKR #KVCache #LLMInference #Meetup
한국어 후기는 @pytorch.kr 블로그에서, 공개된 행사 영상은 PyTorchKR YouTube에서 보실 수 있습니다:
* 한국어 블로그: pytorch.kr/blog/2026/kv...
* KVCache Meetup 영상: www.youtube.com/playlist?lis...
#PyTorch #PyTorchKR #KVCache #LLMInference #Meetup
November 3, 2026 at 11:36 AM
KVCache Meetup Korea의 후기가 @pytorch.org Foundation 공식 블로그에 게재되었습니다! 🎉
한국어 후기는 @pytorch.kr 블로그에서, 공개된 행사 영상은 PyTorchKR YouTube에서 보실 수 있습니다:
* 한국어 블로그: pytorch.kr/blog/2026/kv...
* KVCache Meetup 영상: www.youtube.com/playlist?lis...
#PyTorch #PyTorchKR #KVCache #LLMInference #Meetup
한국어 후기는 @pytorch.kr 블로그에서, 공개된 행사 영상은 PyTorchKR YouTube에서 보실 수 있습니다:
* 한국어 블로그: pytorch.kr/blog/2026/kv...
* KVCache Meetup 영상: www.youtube.com/playlist?lis...
#PyTorch #PyTorchKR #KVCache #LLMInference #Meetup
re you overpaying for AI inference?
Same workload, 1B output tokens a month, and the bill can land anywhere in a 7x range depending on the model.
Full breakdown with live InferenceBench prices on our LinkedIn. www.linkedin.com/feed/update/...
#LLMInference #AIInfrastructure
Same workload, 1B output tokens a month, and the bill can land anywhere in a 7x range depending on the model.
Full breakdown with live InferenceBench prices on our LinkedIn. www.linkedin.com/feed/update/...
#LLMInference #AIInfrastructure
#llminference #aiinfrastructure #mlops #gpucloud #llmops #aicost #finops #genai #inferencebench #yobitel | Yobitel
Are you overpaying for AI inference?
Same workload, same month, and the bill can land anywhere in a 7x range depending on which model you picked.
Those four figures are live InferenceBench prices, e...
www.linkedin.com
October 29, 2026 at 6:48 AM
re you overpaying for AI inference?
Same workload, 1B output tokens a month, and the bill can land anywhere in a 7x range depending on the model.
Full breakdown with live InferenceBench prices on our LinkedIn. www.linkedin.com/feed/update/...
#LLMInference #AIInfrastructure
Same workload, 1B output tokens a month, and the bill can land anywhere in a 7x range depending on the model.
Full breakdown with live InferenceBench prices on our LinkedIn. www.linkedin.com/feed/update/...
#LLMInference #AIInfrastructure
Meta Description: Compare vLLM, SGLang, TensorRT-LLM, LMDeploy, and llama.cpp to choose the right inference server for production LLMs and AI agents.
#llminference
#llminference
Best Inference Servers for Running Open-Source Models in Production
hackernoon.com
October 26, 2026 at 3:20 PM
Meta Description: Compare vLLM, SGLang, TensorRT-LLM, LMDeploy, and llama.cpp to choose the right inference server for production LLMs and AI agents.
#llminference
#llminference
🚀 llama.cpp b11871 and b11873: JSON Depth Limit and Q2_K on Adreno
Two llama.cpp releases dated 2026-10-25 change different areas.
Inside:
• Endpoints That Answer 400 Past 128 Levels
#LlamaCpp #LLMInference
Full breakdown, every claim linked to its source 👇
Two llama.cpp releases dated 2026-10-25 change different areas.
Inside:
• Endpoints That Answer 400 Past 128 Levels
#LlamaCpp #LLMInference
Full breakdown, every claim linked to its source 👇
llama.cpp b11871 and b11873: JSON Depth Limit and Q2_K on Adreno
llama.cpp b11871 returns HTTP 400 for JSON nested past 128 levels, and b11873 adds initial Q2_K support to the OpenCL backend for Adreno GPUs.
thestacksentinel.com
October 25, 2026 at 10:26 AM
🚀 llama.cpp b11871 and b11873: JSON Depth Limit and Q2_K on Adreno
Two llama.cpp releases dated 2026-10-25 change different areas.
Inside:
• Endpoints That Answer 400 Past 128 Levels
#LlamaCpp #LLMInference
Full breakdown, every claim linked to its source 👇
Two llama.cpp releases dated 2026-10-25 change different areas.
Inside:
• Endpoints That Answer 400 Past 128 Levels
#LlamaCpp #LLMInference
Full breakdown, every claim linked to its source 👇
2.99× vLLM throughput on 4× B300, concurrency 1!
Qwen3.8-27B benchmark. Concurrency 8 hit 1.70×. Warm up before you time.
What concurrency does your server run at?
#LLMInference #OpenSource
Qwen3.8-27B benchmark. Concurrency 8 hit 1.70×. Warm up before you time.
What concurrency does your server run at?
#LLMInference #OpenSource
October 21, 2026 at 9:10 PM
2.99× vLLM throughput on 4× B300, concurrency 1!
Qwen3.8-27B benchmark. Concurrency 8 hit 1.70×. Warm up before you time.
What concurrency does your server run at?
#LLMInference #OpenSource
Qwen3.8-27B benchmark. Concurrency 8 hit 1.70×. Warm up before you time.
What concurrency does your server run at?
#LLMInference #OpenSource
Intel Inference Microservices bring tuned LLM inference to Xeon processors through containers, with no manual hardware optimization needed.
#IntelInferenceMicroservices #LLMInference #IntelXeon #vLLM #Kubernetes #Docker #CrescentIsland
#IntelInferenceMicroservices #LLMInference #IntelXeon #vLLM #Kubernetes #Docker #CrescentIsland
Intel Inference Microservices Bring Tuned LLMs to Xeon
Intel Inference Microservices Bring Tuned LLMs to Xeon Intel has announced Intel Inference Microservices, a set of containerized services for large language model inference. The goal is simple. Businesses can now get tuned model inference on the Xeon processors they already own. They no longer need to spend days on manual hardware optimization. How the Containers Work Each microservice is a standalone container.
dailytechnow.com
October 15, 2026 at 2:20 PM
Intel Inference Microservices bring tuned LLM inference to Xeon processors through containers, with no manual hardware optimization needed.
#IntelInferenceMicroservices #LLMInference #IntelXeon #vLLM #Kubernetes #Docker #CrescentIsland
#IntelInferenceMicroservices #LLMInference #IntelXeon #vLLM #Kubernetes #Docker #CrescentIsland
SparPR is a runtime mechanism that recovers the key-value cache during LLM inference on consumer GPUs by selecting, budgeting, and recomputing blocks on demand, without changing model weights or requiring offline…
#LLMInference #KVCache #ConsumerGPU #EfficientAI
https://arxiv.org/abs/2610.18140
#LLMInference #KVCache #ConsumerGPU #EfficientAI
https://arxiv.org/abs/2610.18140
October 14, 2026 at 8:01 PM
SparPR is a runtime mechanism that recovers the key-value cache during LLM inference on consumer GPUs by selecting, budgeting, and recomputing blocks on demand, without changing model weights or requiring offline…
#LLMInference #KVCache #ConsumerGPU #EfficientAI
https://arxiv.org/abs/2610.18140
#LLMInference #KVCache #ConsumerGPU #EfficientAI
https://arxiv.org/abs/2610.18140
GPU Goldmines offers a framework for tuning WebGPU kernels across devices, targeting matrix multiplication and flash attention for edge LLM inference. The approach uses performance characterization to speed up autotuning…
#WebGPU #LLMInference #EdgeAI #GPUComputing
https://arxiv.org/abs/2610.17321
#WebGPU #LLMInference #EdgeAI #GPUComputing
https://arxiv.org/abs/2610.17321
October 14, 2026 at 2:01 PM
GPU Goldmines offers a framework for tuning WebGPU kernels across devices, targeting matrix multiplication and flash attention for edge LLM inference. The approach uses performance characterization to speed up autotuning…
#WebGPU #LLMInference #EdgeAI #GPUComputing
https://arxiv.org/abs/2610.17321
#WebGPU #LLMInference #EdgeAI #GPUComputing
https://arxiv.org/abs/2610.17321
AWS explains deploying one LLM on two GPU nodes via EKS using Ray LLM Deep Learning Containers, splitting prefill and decode behind a single…
#RayLLM #AWSEKS #LLMInference
https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers/
#RayLLM #AWSEKS #LLMInference
https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers/
October 12, 2026 at 4:01 PM
AWS explains deploying one LLM on two GPU nodes via EKS using Ray LLM Deep Learning Containers, splitting prefill and decode behind a single…
#RayLLM #AWSEKS #LLMInference
https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers/
#RayLLM #AWSEKS #LLMInference
https://aws.amazon.com/blogs/machine-learning/run-multi-node-inference-workloads-using-ray-llm-deep-learning-containers/
SparKV is an adaptive framework that splits KV cache loading between cloud streaming and on-device computation to cut Time-to-First-Token by 1.3x–5.1x for on-device LLM inference. It models per-chunk costs and…
#OnDeviceLLM #EdgeAI #LLMInference #KVCache
https://arxiv.org/abs/2604.21231
#OnDeviceLLM #EdgeAI #LLMInference #KVCache
https://arxiv.org/abs/2604.21231
October 9, 2026 at 6:01 AM
SparKV is an adaptive framework that splits KV cache loading between cloud streaming and on-device computation to cut Time-to-First-Token by 1.3x–5.1x for on-device LLM inference. It models per-chunk costs and…
#OnDeviceLLM #EdgeAI #LLMInference #KVCache
https://arxiv.org/abs/2604.21231
#OnDeviceLLM #EdgeAI #LLMInference #KVCache
https://arxiv.org/abs/2604.21231
New arXiv work analyzes compute-communication trade-offs in tensor, pipeline, and hybrid parallelism for LLM inference, highlighting how prefill and decoding phases each demand distinct strategies across multi-GPU setups.…
#AIInfrastructure #LLMInference #GPUs
https://arxiv.org/abs/2610.05305
#AIInfrastructure #LLMInference #GPUs
https://arxiv.org/abs/2610.05305
October 6, 2026 at 6:01 AM
New arXiv work analyzes compute-communication trade-offs in tensor, pipeline, and hybrid parallelism for LLM inference, highlighting how prefill and decoding phases each demand distinct strategies across multi-GPU setups.…
#AIInfrastructure #LLMInference #GPUs
https://arxiv.org/abs/2610.05305
#AIInfrastructure #LLMInference #GPUs
https://arxiv.org/abs/2610.05305
KV² is a self-refining KV-cache compression method that uses a lightweight proxy scorer to find informative tokens, then only reprocesses that subset for eviction scoring, outperforming baselines as cache budgets tighten.
#KVCache #LLMInference #LongContext
https://arxiv.org/abs/2610.03198
#KVCache #LLMInference #LongContext
https://arxiv.org/abs/2610.03198
October 6, 2026 at 12:01 AM
KV² is a self-refining KV-cache compression method that uses a lightweight proxy scorer to find informative tokens, then only reprocesses that subset for eviction scoring, outperforming baselines as cache budgets tighten.
#KVCache #LLMInference #LongContext
https://arxiv.org/abs/2610.03198
#KVCache #LLMInference #LongContext
https://arxiv.org/abs/2610.03198
AMD’s ROCm 10.1 rolls out hipThreads and hipFile to simplify GPU programming and speed up LLM inference, cutting data‑movement headaches. Curious how this changes your GPU workflow? Dive in! #ROCm101 #hipThreads #LLMinference
🔗 aidailypost.com/news/amds-ro...
🔗 aidailypost.com/news/amds-ro...
October 5, 2026 at 10:10 PM
AMD’s ROCm 10.1 rolls out hipThreads and hipFile to simplify GPU programming and speed up LLM inference, cutting data‑movement headaches. Curious how this changes your GPU workflow? Dive in! #ROCm101 #hipThreads #LLMinference
🔗 aidailypost.com/news/amds-ro...
🔗 aidailypost.com/news/amds-ro...
BitNest is a speculative decoding framework that embeds a low-precision draft directly into a higher-precision LLM's weights, letting both share a single memory representation to speed up inference…
#SpeculativeDecoding #LLMInference #EfficientAI #ModelCompression
https://arxiv.org/abs/2610.02800
#SpeculativeDecoding #LLMInference #EfficientAI #ModelCompression
https://arxiv.org/abs/2610.02800
October 5, 2026 at 2:01 PM
BitNest is a speculative decoding framework that embeds a low-precision draft directly into a higher-precision LLM's weights, letting both share a single memory representation to speed up inference…
#SpeculativeDecoding #LLMInference #EfficientAI #ModelCompression
https://arxiv.org/abs/2610.02800
#SpeculativeDecoding #LLMInference #EfficientAI #ModelCompression
https://arxiv.org/abs/2610.02800
SparseEngine is a sparse-first LLM inference engine using a shared lifecycle contract across 15 attention methods, with Chain Cache and Prefix-Cache Pruning for long-context KV management.
#SparseAttention #LLMInference #KVCache #AIInfrastructure
https://arxiv.org/abs/2609.39068
#SparseAttention #LLMInference #KVCache #AIInfrastructure
https://arxiv.org/abs/2609.39068
October 1, 2026 at 10:01 PM
SparseEngine is a sparse-first LLM inference engine using a shared lifecycle contract across 15 attention methods, with Chain Cache and Prefix-Cache Pruning for long-context KV management.
#SparseAttention #LLMInference #KVCache #AIInfrastructure
https://arxiv.org/abs/2609.39068
#SparseAttention #LLMInference #KVCache #AIInfrastructure
https://arxiv.org/abs/2609.39068
New arXiv work introduces DLFP, a model-free vLLM controller that resizes prefill chunks based on observed decode-latency feedback to cut interference during concurrent inference on a single A100. Strong Qwen3-0.6B BF16 results, though…
#AI #LLMInference #vLLM #GPU
https://arxiv.org/abs/2609.38386
#AI #LLMInference #vLLM #GPU
https://arxiv.org/abs/2609.38386
October 1, 2026 at 10:01 AM
New arXiv work introduces DLFP, a model-free vLLM controller that resizes prefill chunks based on observed decode-latency feedback to cut interference during concurrent inference on a single A100. Strong Qwen3-0.6B BF16 results, though…
#AI #LLMInference #vLLM #GPU
https://arxiv.org/abs/2609.38386
#AI #LLMInference #vLLM #GPU
https://arxiv.org/abs/2609.38386
A new paper proposes AI Greeninferencing, a model that colocates modular AI compute with wind farms to ease grid strain, citing 890+ GW of wind capacity within 50 ms latency of Azure data centers. The…
#AIInfrastructure #RenewableEnergy #LLMInference #DataCenters
https://arxiv.org/abs/2605.23348
#AIInfrastructure #RenewableEnergy #LLMInference #DataCenters
https://arxiv.org/abs/2605.23348
September 29, 2026 at 10:01 AM
A new paper proposes AI Greeninferencing, a model that colocates modular AI compute with wind farms to ease grid strain, citing 890+ GW of wind capacity within 50 ms latency of Azure data centers. The…
#AIInfrastructure #RenewableEnergy #LLMInference #DataCenters
https://arxiv.org/abs/2605.23348
#AIInfrastructure #RenewableEnergy #LLMInference #DataCenters
https://arxiv.org/abs/2605.23348
Just saw AIPerf’s latest benchmark—Qwen3‑0.6B crushing LLM inference speeds. If you care about real‑world AI performance, this dive is a must‑read. Curious how it stacks? Check it out! #AIPerf #Qwen3 #LLMInference
🔗 aidailypost.com/news/aiperf-...
🔗 aidailypost.com/news/aiperf-...
September 18, 2026 at 7:38 PM
Just saw AIPerf’s latest benchmark—Qwen3‑0.6B crushing LLM inference speeds. If you care about real‑world AI performance, this dive is a must‑read. Curious how it stacks? Check it out! #AIPerf #Qwen3 #LLMInference
🔗 aidailypost.com/news/aiperf-...
🔗 aidailypost.com/news/aiperf-...
vLLM vs Ollama: Which Inference Server You Actually Need in 2026
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
vLLM vs Ollama: Which Inference Server You Actually Need in 2026
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and Ollama solve it in opposite ways.
www.alekseialeinikov.com
September 15, 2026 at 6:47 AM
vLLM vs Ollama: Which Inference Server You Actually Need in 2026
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
ollama run llama3 gets a model answering in thirty seconds. Getting that same model to serve 200 concurrent users without falling over is a completely different engineering problem — and vLLM and O…
#vllm #ollama #llminference
TurboFieldfare sets a ~2 GB memory budget first and then rebuilds the Gemma 4 26B-A4B runtime, its per-layer expert cache, and even the installer to live inside it on an 8 GB Mac: https://github.com/drumih/turbo-fieldfare
#Swift #Metal #LlmInference
#Swift #Metal #LlmInference
August 29, 2026 at 4:55 PM
TurboFieldfare sets a ~2 GB memory budget first and then rebuilds the Gemma 4 26B-A4B runtime, its per-layer expert cache, and even the installer to live inside it on an 8 GB Mac: https://github.com/drumih/turbo-fieldfare
#Swift #Metal #LlmInference
#Swift #Metal #LlmInference
kimi-k3-in-c runs a 2.78-trillion-parameter Kimi K3 on one CPU in 8.24 GB of RAM, with byte-identical output from 8 GB up to 224 GB, so memory buys speed rather than eligibility: https://github.com/FareedKhan-dev/kimi-k3-in-c
#C #LlmInference #CpuInference
#C #LlmInference #CpuInference
August 28, 2026 at 10:48 PM
kimi-k3-in-c runs a 2.78-trillion-parameter Kimi K3 on one CPU in 8.24 GB of RAM, with byte-identical output from 8 GB up to 224 GB, so memory buys speed rather than eligibility: https://github.com/FareedKhan-dev/kimi-k3-in-c
#C #LlmInference #CpuInference
#C #LlmInference #CpuInference
Can an old consumer PC run large LLMs better than we assume? My DeepSeek V4 experiments challenged the idea that disk streaming was the real performance wall. #llminference
Your Old PC Can Run LLMs: The Real Bottleneck Is Optimization
hackernoon.com
August 27, 2026 at 4:36 AM
Can an old consumer PC run large LLMs better than we assume? My DeepSeek V4 experiments challenged the idea that disk streaming was the real performance wall. #llminference