multi-gpu

Wan-Streamer v0.2 is a latency-preserving upgrade to an end-to-end audio-visual interaction model, increasing output resolution from 192x336 to 640x368 while maintaining ~200 ms model-side latency via a multi-GPU thinker-performer architecture.

A report on running the GLM5.2 language model across 5 AMD Radeon Pro 6000 GPUs and an NVIDIA RTX 5090, detailing the high cost and technical challenges.

Ahmad (@TheAhmadOsman) announces a blogpost covering inference engines like llama.cpp, vLLM, and ExLlamaV2, focusing on multi-GPU setups, tensor parallelism, and batch inference for optimized AI model performance.

Technical report on running Qwen 3.6 27B Q8 model on a dual AMD Radeon R9700 setup using llama.cpp with ROCm, including performance benchmarks and configuration details.

A user shares optimized settings for running Qwen3.6 27B (Q8_0) on a dual GPU setup (RTX 4090 + RTX 3090) with llama.cpp, achieving 75-100 t/s and 1500 pp with 250k context.

A discussion on the cheapest local hardware setups for running GLM 5.x and similarly sized models at 4-bit quantization, including CPU-only and multi-GPU options, with a user sharing their experience running Minimax 2.7 and Qwen 3.6 on a 5900X + 128GB DDR4 + 7900XT setup.

The author discovered that compiling llama.cpp with both CUDA and Vulkan backends simultaneously is possible, yielding a ~10% improvement in tokens/sec for decoding. They plan to run further benchmarks to assess the benefits.

A user discovered that a hidden PCIe 2.0 x4 electrical limitation on a Threadripper workstation board was crippling one of four RTX 3090s, causing poor multi-GPU LLM inference performance. Fixing the slot layout and switching to tensor split mode doubled Mistral 128B throughput from ~11 to ~24.7 tok/s.

llama.cpp maintainers and NVIDIA engineers collaborated to significantly improve multi-GPU performance in ggml, enabling hardware-agnostic tensor parallelism and major performance gains on RTX systems.

User reports a CUDA error when using tensor split mode with the latest llama.cpp and Qwen-3.6-27b model on dual RTX 3090s with Ubuntu Server 24.04 and Docker.

llama.cpp version b9455 merges a fix for `-sm tensor` KV cache quantization on multi-GPU setups, addressing a shape information loss issue when flattening tensors.

Llama.cpp is expected to receive a fix for split mode tensor crashes on multi-GPU setups, which currently cause VRAM exhaustion every 90-120 minutes. The fix also reportedly brings a ~35% throughput improvement over layer mode.

A learner shares enthusiasm for Stanford CS336 lecture 7 on GPU parallelism, which covers fundamental operations and connects them to multi-GPU setups and parallelism techniques like tensor, data, and pipeline parallelism.

A grad student shares their experience building a multi-GPU workstation with 4x3090 Ti running on a single US wall outlet, detailing constraints, power-limiting challenges, and component choices.

EnergyLens is an end-to-end framework for predictive energy-aware optimization of multi-GPU LLM inference, validated on Llama3 and Qwen3-MoE, achieving mean absolute percentage errors between 9.25% and 13.19% and revealing significant energy variation across configurations.

llama.cpp build b9095 introduces NCCL-free tensor parallelism for dual Blackwell PCIe GPUs, enabling efficient multi-GPU inference without relying on NCCL.

User benchmarks Qwen3.6-27B-Q8_0 at ~13 tokens/sec on 3 mixed GPUs with 128k context via llama.cpp, asking if performance is typical.