Find which open-weight LLMs fit in your GPU or Mac and compare the speeds people report on setups like yours.
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These are individual setups, not a controlled benchmark. Compare GPU count, quantization, context and offloading before comparing speeds. How to read a report →
Exact recorded values. Context may be a configured limit; matching filters does not establish identical prompts, offloading or concurrency.
NVIDIA GTX 1080 Ti · llama.cpp
User benchmarks Llama-2 7B at 62 t/s generation and 1084 t/s prompt processing on an NVIDIA GTX 1080 Ti 11GB. Setup is the CUDA build of llama.cpp with Q4_0 GGUF weights, run via llama-bench with -ngl 100. Figures are pp512 and tg128, a plain single-stream run with no speculative decoding.
2× NVIDIA GTX 1080 Ti · llama.cpp
User reports Gemma 4 26B-A4B at 52.03 t/s generation and 340.86 t/s prompt processing on a dual-GPU setup of GTX 1080 Ti and Radeon MI50 16GB, totaling 27GB VRAM. Setup is llama.cpp Vulkan pre-built binary build 851cb34f2 (11055) with the UD-Q6_K_XL GGUF, flash attention on, and 99 GPU layers offloaded. Single-GPU GTX 1080 Ti runs of the same model reached 11.76 t/s generation and 162.12 t/s prompt processing. The user also benchmarked Qwen3.6-35B-A3B MXFP4 MoE, Nemotron 31B-A3.5B Q5_K_M, Qwen3.8 27B Q6_K, and medgemma 27B Q6_K_XL, noting dense models benefited most from the second GPU.
2× NVIDIA GTX 1080 Ti · llama.cpp
User benchmarks Qwen3.6 35B-A3B at 19.7 t/s generation and 332.57 t/s prompt processing on a mixed GTX 1080 Ti and Radeon VII Vulkan setup. Setup is llama.cpp Vulkan build b29c606e2 with the MXFP4 MoE GGUF and Flash Attention enabled, across two GPUs. User also reports results for twelve other models including llama 7B, bailingmoe2 16B-A1B, gpt-oss 20B, Gemma 4 26B-A4B, Qwen3.5 27B, Qwen3-Coder 30B-A3B, granite 4.0, and Phi-3.5-MoE.
On Qwen3.8 27B at 4-bit, plain single-GPU runs. Full ranking