Видео с ютуба Max_Num_Seqs

vLLM Capacity Planning: What max_num_seqs Actually Does

vLLM Capacity Planning: What max_num_seqs Actually Does
Оптимизация, развертывание и бенчмаркинг LLM с открытым исходным кодом с помощью vLLM

Оптимизация, развертывание и бенчмаркинг LLM с открытым исходным кодом с помощью vLLM
Get More Performance From Your DGX Spark — vLLM + Grafana Tuning Dashboard

Get More Performance From Your DGX Spark — vLLM + Grafana Tuning Dashboard
vLLM Explained in 10 Min: 3 Settings for Insanely Fast Throughput & Latency!

vLLM Explained in 10 Min: 3 Settings for Insanely Fast Throughput & Latency!
Optimizing Qwen 3.5 Vision SPEED AI Locally: vLLM, Docker & Preprocessing Deep Dive. Insane results!

Optimizing Qwen 3.5 Vision SPEED AI Locally: vLLM, Docker & Preprocessing Deep Dive. Insane results!
The KV Cache Layer That Makes LLMs 10x Faster? (LMCache)

The KV Cache Layer That Makes LLMs 10x Faster? (LMCache)
I Bought an NVIDIA DGX Spark... Here's What It Actually Does (90+ Tokens/sec)

I Bought an NVIDIA DGX Spark... Here's What It Actually Does (90+ Tokens/sec)
DeepSeek V4 Flash 0731 on 2x RTX PRO 6000 | vLLM + DSpark K5 + Native FP4 Experts

DeepSeek V4 Flash 0731 on 2x RTX PRO 6000 | vLLM + DSpark K5 + Native FP4 Experts
Gemma4-31B QAT W4A16 on RTX 5090 at 670+ TPS 15+ req. Best Local Setup For Small Business

Gemma4-31B QAT W4A16 on RTX 5090 at 670+ TPS 15+ req. Best Local Setup For Small Business
Qwen3.6-27B NVFP4+MTP vLLM Benchmark TG 190tok/s — RTX PRO 6000 Blackwell Max-Q x 2

Qwen3.6-27B NVFP4+MTP vLLM Benchmark TG 190tok/s — RTX PRO 6000 Blackwell Max-Q x 2
Nemotron 3 Super 120B NVFP4 on RTX PRO 6000: 150 tok/s with vLLM MTP

Nemotron 3 Super 120B NVFP4 on RTX PRO 6000: 150 tok/s with vLLM MTP
Production-Ready Local LLM for SMB: 760+ TPS on a Single Consumer GPU to Eliminate API Subscriptions

Production-Ready Local LLM for SMB: 760+ TPS on a Single Consumer GPU to Eliminate API Subscriptions
Qwen3.8-Flash-Next 125B: 60 токенов/с на MLX против 30 токенов/с на llama.cpp на M5 Max 🚀

Qwen3.8-Flash-Next 125B: 60 токенов/с на MLX против 30 токенов/с на llama.cpp на M5 Max 🚀
[follow-up] Laguna S2.1 August update on Single DGX spark

[follow-up] Laguna S2.1 August update on Single DGX spark

S.T.A.L.K.E.R. G.A.M.M.A. 0.9.5. Максимальная сложность # 008

S.T.A.L.K.E.R. G.A.M.M.A. 0.9.5. Максимальная сложность # 008
Editing code on server using Qwen3 Coder with Cline on Dell with 4x V100 Nvidia GPU - Private LLM

Editing code on server using Qwen3 Coder with Cline on Dell with 4x V100 Nvidia GPU - Private LLM
Continuous Batching Explained: Iteration-Level Scheduling in vLLM (Orca Paper)

Continuous Batching Explained: Iteration-Level Scheduling in vLLM (Orca Paper)
Running Gemma4 31B FP8 and NVFP4 with MTP locally on an RTX PRO 6000 Blackwell

Running Gemma4 31B FP8 and NVFP4 with MTP locally on an RTX PRO 6000 Blackwell
초보자도 가능한 vLLM을 200% 활용하기 위한 Argument 설정법!

초보자도 가능한 vLLM을 200% 활용하기 위한 Argument 설정법!