llama.cpp: owners of old GPUs wanted for performance testing

OK, I follow you on that, any reason why llama.cpp b3266 won't force MMQ then? llm\_load\_print\_meta: model type = 8B llm\_load\_print\_meta: model ftype = Q6\_K llm\_load\_print\_meta: model params = 8.03 B llm\_load\_print\_meta: model size = 6.14 GiB (6.56 BPW) llm\_load\_print\_meta: [general.name](http://general.name)= Meta-Llama-3-8B-Instruct llm\_load\_print\_meta: BOS token = 128000 '<|begin\_of\_text|>' llm\_load\_print\_meta: EOS token = 128001 '<|end\_of\_text|>' llm\_load\_print\_meta: LF token = 128 'Ä' llm\_load\_print\_meta: EOT token = 128009 '<|eot\_id|>' llm\_load\_print\_meta: max token length = 256 ggml\_cuda\_init: GGML\_CUDA\_FORCE\_MMQ: no ggml\_cuda\_init: GGML\_CUDA\_FORCE\_CUBLAS: no ggml\_cuda\_init: found 1 CUDA devices: Device 0: Quadro P5000, compute capability 6.1, VMM: yes llm\_load\_tensors: ggml ctx size = 0.27 MiB llm\_load\_tensors: offloading 32 repeating layers to GPU llm\_load\_tensors: offloading non-repeating layers to GPU llm\_load\_tensors: offloaded 33/33 layers to GPU llm\_load\_tensors: CPU buffer size = 410.98 MiB llm\_load\_tensors: CUDA0 buffer size = 5871.99 MiB ......................................................................................... llama\_new\_context\_with\_model: n\_ctx = 8192 llama\_new\_context\_with\_model: n\_batch = 2048 llama\_new\_context\_with\_model: n\_ubatch = 512 llama\_new\_context\_with\_model: flash\_attn = 0 llama\_new\_context\_with\_model: freq\_base = 500000.0 llama\_new\_context\_with\_model: freq\_scale = 1 llama\_kv\_cache\_init: CUDA0 KV buffer size = 1024.00 MiB llama\_new\_context\_with\_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB llama\_new\_context\_with\_model: CUDA\_Host output buffer size = 0.49 MiB llama\_new\_context\_with\_model: CUDA0 compute buffer size = 560.00 MiB llama\_new\_context\_with\_model: CUDA\_Host compute buffer size = 24.01 MiB llama\_new\_context\_with\_model: graph nodes = 1030 llama\_new\_context\_with\_model: graph splits = 2