ik_llama.cpp/github-data/discussions/548-Poor performance with bf16 model on Qwen3 30B-A3B.md

🗣️ #548 - Poor performance with bf16 model on Qwen3 30B-A3B

Author	Gaolingx
Created	2025-06-22
Updated	2025-07-02

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Description

Introduction

I tried to run model Qwen3-30B-A3B-GGUF with ik_llama.cpp. Because I have a nvidia GPU(RTX 4060Ti) with 8G VRAM on my PC, so I compiled ik_llama.cpp with the cuda backend, and run with -ot exps=CPU to offload experts(ffn_down_exps, ffn_up_exps, gate_exps) to CPU.

Build options:

cmake -B build -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_CUDA=ON -DGGML_AVX2=ON -DGGML_AVX512=OFF -DBUILD_SHARED_LIBS=ON

I tested q8_0 quantization and bf16 models, on q8_0 model, the prompt processing speed(PP) the token generate speed(TG) are very quickly, I got a speed of up to 165 token/s PP and 18 token/s TG, that's a good start. but when I ran bf16 model, the PP speed is much slower than before, It just 30-40token/s PP, 11-12 token/s TG, It's not even as good as only CPU ggml backend(about 51 token/s PP, 11 token/s TG), This performance is obviously not normal on bf16 models. It makes me confused. I've also found that the GPU spends quite a bit of time on the copy every time the token processing phase is processed, but quantization modes(like q8_0) don't have the above problem.

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cpu backend, bf16 model(Qwen3-30B-A3B-BF16)

cuda backend, bf16 model(Qwen3-30B-A3B-BF16)

cuda backend, q8_0 model(Qwen3-30B-A3B-Q8_0)

System Info

Here are my SystemInfo(include hardware and software)

• Hardware
  • CPU: Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz(20c, 40t) x2
  • GPU: NVIDIA GeForce RTX 4060Ti 8G
  • RAM: RDIMM DDR4 2666 2Rx4 32G x16(12 Channels total)
  • Motherboard: Supermicro X11DPi-N
  • SSD: ZHITAI TiPlus7100 1TB
• Software
  • OS: Microsoft Windows 10 Pro
  • BIOS: Hyper-Threading-Enable, SNC-Disable
  • Model: Qwen3-235B-A22B-128K-Q8_0(unsloth/Qwen3-235B-A22B-128K-GGUF)
  • ik_llama.cpp:

  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: NVIDIA GeForce RTX 4060 Ti, compute capability 8.9, VMM: yes
  INFO [                    main] build info | tid="54808" timestamp=1750526676 build=3761 commit="144ee1c4"
  INFO [                    main] system info | tid="54808" timestamp=1750526676 n_threads=16 n_threads_batch=-1 total_threads=40 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | "
  

Benchmark

Here are the results of my initialllama-sweep-bench testing for PP speed and TG speed, the command line for is ik_llama.cpp

llama-sweep-bench:

./llama-sweep-bench -m "%MODEL_PATH%" -c 16384 -t 16 -ngl 48 -fa -rtr -fmoe -ser 6,1 -ot exps=CPU

ik_llama.cpp cuda backed (Model: Qwen3-30B-A3B-Q8_0)

main: n_kv_max = 16384, n_batch = 2048, n_ubatch = 512, flash_attn = 1, n_gpu_layers = 48, n_threads = 16, n_threads_batch = 16

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	2.308	221.84	6.463	19.81
512	128	512	2.437	210.11	7.741	16.54
512	128	1024	2.295	223.07	7.040	18.18
512	128	1536	2.537	201.81	7.739	16.54
512	128	2048	2.327	220.05	7.006	18.27
512	128	2560	2.523	202.97	7.766	16.48
512	128	3072	2.571	199.15	7.901	16.20
512	128	3584	2.531	202.26	7.717	16.59
512	128	4096	2.600	196.89	8.016	15.97
512	128	4608	2.602	196.80	7.962	16.08
512	128	5120	2.623	195.21	7.880	16.24
512	128	5632	2.614	195.86	8.090	15.82
512	128	6144	2.647	193.44	8.055	15.89
512	128	6656	2.647	193.43	7.963	16.07
512	128	7168	2.686	190.62	7.975	16.05
512	128	7680	2.687	190.54	8.069	15.86
512	128	8192	2.691	190.28	7.990	16.02
512	128	8704	2.713	188.69	8.030	15.94
512	128	9216	2.690	190.33	8.081	15.84
512	128	9728	2.706	189.24	8.015	15.97
512	128	10240	2.712	188.80	8.034	15.93
512	128	10752	2.777	184.35	8.097	15.81
512	128	11264	2.728	187.69	8.142	15.72
512	128	11776	2.651	193.15	8.040	15.92
512	128	12288	2.715	188.57	8.032	15.94
512	128	12800	2.727	187.76	8.091	15.82
512	128	13312	2.693	190.12	8.145	15.72
512	128	13824	2.692	190.22	8.137	15.73
512	128	14336	2.579	198.54	7.770	16.47
512	128	14848	2.688	190.45	8.211	15.59
512	128	15360	2.592	197.57	8.075	15.85
512	128	15872	2.660	192.47	8.132	15.74

ik_llama.cpp cuda backed (Model: Qwen3-30B-A3B-BF16)

main: n_kv_max = 16384, n_batch = 2048, n_ubatch = 512, flash_attn = 1, n_gpu_layers = 48, n_threads = 16, n_threads_batch = 16

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	18.004	28.44	10.550	12.13
512	128	512	17.938	28.54	10.384	12.33
512	128	1024	17.859	28.67	10.370	12.34
512	128	1536	17.924	28.57	10.399	12.31
512	128	2048	17.989	28.46	10.386	12.32
512	128	2560	17.935	28.55	10.435	12.27
512	128	3072	18.006	28.44	10.513	12.18
512	128	3584	18.030	28.40	10.495	12.20
512	128	4096	18.063	28.35	10.578	12.10
512	128	4608	17.570	29.14	10.613	12.06
512	128	5120	17.685	28.95	10.600	12.08
512	128	5632	17.744	28.86	10.682	11.98
512	128	6144	17.911	28.59	10.640	12.03
512	128	6656	17.727	28.88	10.719	11.94
512	128	7168	17.529	29.21	10.636	12.03
512	128	7680	17.547	29.18	10.660	12.01
512	128	8192	17.517	29.23	10.708	11.95
512	128	8704	17.572	29.14	10.814	11.84
512	128	9216	17.542	29.19	10.813	11.84
512	128	9728	17.615	29.07	10.815	11.84
512	128	10240	17.573	29.14	10.839	11.81
512	128	10752	17.616	29.06	10.858	11.79
512	128	11264	17.670	28.98	10.899	11.74
512	128	11776	17.764	28.82	11.194	11.44
512	128	12288	17.622	29.05	10.960	11.68
512	128	12800	17.658	28.99	11.039	11.60
512	128	13312	17.661	28.99	11.036	11.60
512	128	13824	17.624	29.05	11.093	11.54
512	128	14336	17.587	29.11	11.094	11.54
512	128	14848	17.650	29.01	11.174	11.45
512	128	15360	17.648	29.01	11.190	11.44
512	128	15872	17.645	29.02	11.204	11.42

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🗣️ Discussion

👤 ikawrakow replied the 2025-06-22 at 15:16:00:

Don't use -rtr for the bf16 model.

👤 Gaolingx replied the 2025-06-22 at 15:31:07:
wow, thanks a lot for your suggestion, the speed is normal now, I got ~65 PP speed and ~11.8 TG speed, but the cpu+cuda(-ot exps=CPU) speed doesn't seem to be much faster than the pure cpu, although it is a moe model. maybe I should do a more detailed benchmark.

👤 ikawrakow replied the 2025-06-22 at 15:35:13:
You need larger u-batch size for better PP performance. The experts are in RAM and need to be offloaded to the GPU, which takes a while. If you run llama-sweep-bench with -ub 2048 you will see much better PP performance.

👤 Gaolingx replied the 2025-07-02 at 10:54:41:
Hi, we all know that runtime repack(-rtr) is good to use with hybrid GPU + CPU, according to my research in the last few days, if we don't add the '-rtr' parameter, when we input long prompts, the cuda device needs to spend a long time on copying (and you can see in the task manager that the usage of 'Copy1' is quite high, but the usage of CPU and CUDA is insufficient), and the processing speed of prompt words is also significantly lower than the performance with the '-rtr' parameter, or even worse than the cpu only, what is the reason for this?

👤 ikawrakow replied the 2025-07-02 at 12:14:25:
I'm not sure I understand what could be the issue from the description. Can you tell us what is the model you are using and post your command line?

👤 Gaolingx replied the 2025-07-02 at 14:23:58:

I'm not sure I understand what could be the issue from the description. Can you tell us what is the model you are using and post your command line?

Ok. I ran llama-sweep-bench again and tested the 16k context length data of three sets of qwen3 30ba3b models. They are that the q8_0 model with -rtr parameter, the q8_0 model without -rtr parameter, and the bf16 model without -rtr parameter. To control the variables, in the test group without the -rtr parameter, I added the --no-mmap parameter. The rest of the startup parameters remained the same. The llama-sweep-bench startup parameters and test results are as follows.

I have come to the conclusion that, whether it is the q8_0 or bf16 model, on my platform, if the -rtr parameter is not used, the prompt processing performance during cpu+gpu hybrid inference will be significantly affected. The larger the model, the more serious this situation is. However, The token generation speed is normal and in line with expectations. What causes this? How does the runtime repack tensors(-rtr) to improve prompt processing performance?

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ik_llama.cpp cuda backed (Model: Qwen3-30B-A3B-Q8_0 with -rtr)

./llama-sweep-bench -m "D:\Downloads\Qwen3-30B-A3B-Q8_0.gguf" -c 16384 -t 16 -ngl 49 -fa -rtr -fmoe -ser 6,1 -ot exps=CPU

main: n_kv_max = 16384, n_batch = 2048, n_ubatch = 512, flash_attn = 1, n_gpu_layers = 49, n_threads = 16, n_threads_batch = 16

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	2.247	227.87	5.941	21.54
512	128	512	2.293	223.28	6.718	19.05
512	128	1024	2.354	217.46	6.981	18.34
512	128	1536	2.382	214.94	7.088	18.06
512	128	2048	2.406	212.81	7.011	18.26
512	128	2560	2.394	213.84	7.078	18.09
512	128	3072	2.408	212.61	7.080	18.08
512	128	3584	2.383	214.83	7.127	17.96
512	128	4096	2.415	211.97	7.083	18.07
512	128	4608	2.391	214.12	7.170	17.85
512	128	5120	2.461	208.03	7.216	17.74
512	128	5632	2.448	209.11	7.233	17.70
512	128	6144	2.458	208.31	7.286	17.57
512	128	6656	2.456	208.48	7.251	17.65
512	128	7168	2.413	212.17	7.160	17.88
512	128	7680	2.450	208.98	7.310	17.51
512	128	8192	2.482	206.26	7.302	17.53
512	128	8704	2.365	216.50	7.130	17.95
512	128	9216	2.371	215.94	7.109	18.01
512	128	9728	2.381	214.99	7.264	17.62
512	128	10240	2.395	213.81	7.192	17.80
512	128	10752	2.402	213.16	7.103	18.02
512	128	11264	2.402	213.18	7.005	18.27
512	128	11776	2.372	215.87	7.023	18.22
512	128	12288	2.474	206.92	6.762	18.93
512	128	12800	2.457	208.42	6.808	18.80
512	128	13312	2.442	209.64	6.740	18.99
512	128	13824	2.447	209.22	6.824	18.76
512	128	14336	2.473	207.03	6.704	19.09
512	128	14848	2.524	202.86	6.695	19.12
512	128	15360	2.573	199.00	7.093	18.05
512	128	15872	2.520	203.17	6.611	19.36

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ik_llama.cpp cuda backed (Model: Qwen3-30B-A3B-Q8_0 without -rtr)

./llama-sweep-bench -m "D:\Downloads\Qwen3-30B-A3B-Q8_0.gguf" -c 16384 -t 16 -ngl 49 -fa --no-mmap -fmoe -ser 6,1 -ot exps=CPU

main: n_kv_max = 16384, n_batch = 2048, n_ubatch = 512, flash_attn = 1, n_gpu_layers = 49, n_threads = 16, n_threads_batch = 16

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	9.527	53.74	6.171	20.74
512	128	512	9.556	53.58	6.117	20.93
512	128	1024	9.554	53.59	6.184	20.70
512	128	1536	9.551	53.61	6.149	20.82
512	128	2048	9.590	53.39	6.255	20.46
512	128	2560	9.523	53.76	6.230	20.55
512	128	3072	9.509	53.84	6.257	20.46
512	128	3584	9.555	53.58	6.274	20.40
512	128	4096	9.640	53.11	6.705	19.09
512	128	4608	9.638	53.12	6.409	19.97
512	128	5120	9.615	53.25	6.388	20.04
512	128	5632	9.652	53.04	6.360	20.12
512	128	6144	9.662	52.99	6.430	19.91
512	128	6656	9.702	52.77	6.480	19.75
512	128	7168	9.609	53.28	6.494	19.71
512	128	7680	9.606	53.30	6.485	19.74
512	128	8192	9.622	53.21	6.521	19.63
512	128	8704	9.620	53.22	6.546	19.55
512	128	9216	9.559	53.56	6.602	19.39
512	128	9728	9.538	53.68	6.542	19.57
512	128	10240	9.563	53.54	6.626	19.32
512	128	10752	9.610	53.28	6.561	19.51
512	128	11264	9.689	52.85	6.618	19.34
512	128	11776	9.619	53.23	6.628	19.31
512	128	12288	9.654	53.03	6.452	19.84
512	128	12800	9.800	52.24	6.578	19.46
512	128	13312	9.641	53.11	6.613	19.35
512	128	13824	9.638	53.12	6.513	19.65
512	128	14336	9.686	52.86	6.555	19.53
512	128	14848	9.729	52.62	6.609	19.37
512	128	15360	9.702	52.77	6.624	19.32
512	128	15872	9.697	52.80	6.636	19.29

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ik_llama.cpp cuda backed (Model: Qwen3-30B-A3B-BF16 without -rtr)

./llama-sweep-bench -m "D:\Downloads\unsloth\Qwen3-30B-A3B-GGUF\BF16\Qwen3-30B-A3B-BF16-00001-of-00002.gguf" -c 16384 -t 16 -ngl 49 -fa --no-mmap -fmoe -ser 6,1 -ot exps=CPU

main: n_kv_max = 16384, n_batch = 2048, n_ubatch = 512, flash_attn = 1, n_gpu_layers = 49, n_threads = 16, n_threads_batch = 16

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	17.771	28.81	9.791	13.07
512	128	512	17.398	29.43	9.025	14.18
512	128	1024	17.305	29.59	9.030	14.17
512	128	1536	17.367	29.48	9.054	14.14
512	128	2048	17.859	28.67	9.342	13.70
512	128	2560	17.700	28.93	9.143	14.00
512	128	3072	17.696	28.93	9.170	13.96
512	128	3584	17.939	28.54	9.241	13.85
512	128	4096	17.926	28.56	9.212	13.89
512	128	4608	17.714	28.90	9.280	13.79
512	128	5120	17.822	28.73	9.226	13.87
512	128	5632	17.830	28.72	9.273	13.80
512	128	6144	17.749	28.85	9.121	14.03
512	128	6656	17.581	29.12	9.356	13.68
512	128	7168	17.517	29.23	9.401	13.62
512	128	7680	17.408	29.41	9.393	13.63
512	128	8192	17.451	29.34	9.371	13.66
512	128	8704	17.409	29.41	9.544	13.41
512	128	9216	17.443	29.35	9.476	13.51
512	128	9728	17.449	29.34	10.037	12.75
512	128	10240	17.370	29.48	9.480	13.50
512	128	10752	17.472	29.30	9.504	13.47
512	128	11264	17.612	29.07	9.500	13.47
512	128	11776	17.492	29.27	9.580	13.36
512	128	12288	17.384	29.45	9.569	13.38
512	128	12800	18.000	28.44	9.436	13.56
512	128	13312	17.759	28.83	9.493	13.48
512	128	13824	17.905	28.60	9.442	13.56
512	128	14336	17.843	28.69	9.372	13.66
512	128	14848	17.928	28.56	9.538	13.42
512	128	15360	17.902	28.60	9.436	13.57
512	128	15872	17.971	28.49	9.336	13.71

👤 ikawrakow replied the 2025-07-02 at 14:40:43:
When you use -rtr, the tensors not offloaded to the GPU get repacked to a row-interleaved version. Q8_0 becomes Q8_0_R8, and BF16 becomes BF16_R16. Q8_0_R8 and BF16_R16 are not supported by the CUDA backend, so matrix multiplications with these tensors are done on the CPU. When you do not use -rtr, there is no repacking, CUDA supports Q8_0 and BF16, so the tensors stored in RAM get copied to the GPU to do matrix multiplications. If the model is large, and your PCI-E is not very fast, the copying to VRAM takes a long time, so your PP performance becomes low. You can improve the performance by using larger u-batches because more work is done per copy to the GPU (tensors are copied once, but multiply 2048 tokens with -ub 2048. To accomplish the same with the u-batch of 512 you are using, tensors need to get copied 4 times). If you don't want to repack, and don't want to use larger u-batches, you can prevent copying to the GPU using -op 26,0,27,0,29,0. In that case bf16 performance will be slightly lower than with -rtr, but Q8_0 performance will be somewhere in the middle between -rtr and no -rtr.