Qwen3.8 27B Q5_K_M + 7900xtx + DeepSeek Harness实战平均 52 t/s
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为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
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为啥我的启动参数是这样：
-m "$SELECTED_MODEL" -t 10 -b 512 -ub 256
--spec-draft-n-max 3 --fit off --no-context-shift
--metrics --kv-unified --jinja
--cache-type-k q8_0 --cache-type-v q8_0
-fa on --spec-type draft-mtp
--ctx-size 131072 --parallel 1 -ngl -1
--host 0.0.0.0 --port $LLAMA_PORT
--chat-template-kwargs {"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}
--no-mmap --temp 0.6 --top-p 0.5 --top-k 15 --repeat-penalty 1.0
--alias qwen3.8-27b-q5
仍然速度很低，只有12左右
配置如下
cpu： AMD Ryzen 7 2700 Eight-Core Processor (8核16线程, 3.2GHz)
RAM： 64GB DDR4
GPU： AMD Radeon RX 7900 XTX 24GB (RADV NAVI31)
OS： Ubuntu 26.04 LTS (内核 7.0.0-29-generic)
软件 ： llama.cpp Vulkan b10486 -
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感谢楼主. 7900xtx 能跑,不错. 明天试试dsh. 今天dsh+v4pro花了100多, 肉疼(不过确实强, 长链自主调试FPGA, 综合+ila probe+烧写+测试+uart输出分析一条龙, 我基本可以不用管, 花token就行).
bruin@lmde7 ~ $ ./run-3.8-q5.sh 0.00.035.069 W Setting 'enable_thinking' via --chat-template-kwargs is deprecated. Use --reasoning on / --reasoning off instead. 0.00.035.097 W DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead 0.00.039.005 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.039.491 W srv llama_server: ----------------- 0.00.039.495 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set 0.00.039.495 W srv llama_server: this can be a security risk (cross-origin attacks) 0.00.039.495 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655 0.00.039.495 W srv llama_server: ----------------- 0.00.040.765 I srv load_model: loading model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.08.836.391 I cmn init: llama threadpool init, n_threads = 10 0.09.247.607 I common_speculative_init_result: creating MTP draft context against the target model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.09.318.357 I srv load_model: initializing, n_slots = 1, n_ctx_slot = 131072, kv_unified = 'true' 0.09.457.688 I srv init: chat template supports preserving reasoning, consider enabling it via --reasoning-preserve 0.09.457.746 I srv llama_server: model loaded 0.09.457.750 I srv llama_server: listening on http://0.0.0.0:8000 0.31.502.907 I slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1 0.31.503.144 I slot launch_slot_: id 0 | task 0 | processing task, is_child = 0 0.33.674.917 I slot print_timing: id 0 | task 0 | prompt eval time = 1607.08 ms / 333 tokens ( 4.83 ms per token, 207.21 tokens per second) 0.33.674.922 I slot print_timing: id 0 | task 0 | eval time = 564.47 ms / 34 tokens ( 17.11 ms per token, 58.46 tokens per second) 0.33.674.923 I slot print_timing: id 0 | task 0 | total time = 2171.56 ms / 367 tokens 0.33.674.928 I slot print_timing: id 0 | task 0 | graphs reused = 11 0.33.674.930 I slot print_timing: id 0 | task 0 | draft acceptance = 0.72727 ( 24 accepted / 33 generated), mean len = 3.18 0.33.674.999 I slot release: id 0 | task 0 | stop processing: n_tokens = 368, truncated = 0 0.39.250.971 I slot get_availabl: id 0 | task -1 | selected slot by LCP similarity, f_sim_best = 0.963 (> 0.100 thold), f_keep = 1.000 0.39.251.208 I slot launch_slot_: id 0 | task 16 | processing task, is_child = 0 0.40.592.295 I slot print_timing: id 0 | task 16 | prompt eval time = 368.90 ms / 14 tokens ( 26.35 ms per token, 37.95 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | eval time = 972.01 ms / 67 tokens ( 14.73 ms per token, 67.90 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | total time = 1340.91 ms / 81 tokens 0.40.592.305 I slot print_timing: id 0 | task 16 | graphs reused = 31 0.40.592.307 I slot print_timing: id 0 | task 16 | draft acceptance = 0.71429 ( 45 accepted / 63 generated), mean len = 3.14 0.40.592.367 I slot release: id 0 | task 16 | stop processing: n_tokens = 448, truncated = 0llama.cpp 我用的最新 b10485; 完全抄作业:
bruin@lmde7 ~ $ cat run-3.8-q5.sh #!/bin/bash # ref: https://lcz.me/topic/1157/qwen3.8-27b-q5_k_m-7900xtx-deepseek-harness%E5%AE%9E%E6%88%98%E5%B9%B3%E5%9D%87-52-t-s LLAMA_SERVER=/home/bruin/llama-server-vulkan-b10485 MAIN_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf" MTMD_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/mmproj-F16.gguf" #--mmproj "${MTMD_MODEL}" \ ${LLAMA_SERVER} \ --device Vulkan0 \ --model "${MAIN_MODEL}" \ -t 10 \ -b 512 \ -ub 256 \ --spec-draft-n-max 3 \ --fit off \ --no-context-shift \ --metrics \ --kv-unified \ --jinja \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ -fa on \ --spec-type draft-mtp \ --ctx-size 131072 \ --parallel 1 \ -ngl -1 \ --host 0.0.0.0 \ --port 8000 \ --chat-template-kwargs '{"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}' \ --no-mmap \ --temp 0.6 \ --top-p 0.5 \ --top-k 15 \ --repeat-penalty 1.0 \ --override-tensor blk\.\d+\.ffn_.*_exps\.=CPU \ --alias qwen3.8-27b-q5
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感谢楼主. 7900xtx 能跑,不错. 明天试试dsh. 今天dsh+v4pro花了100多, 肉疼(不过确实强, 长链自主调试FPGA, 综合+ila probe+烧写+测试+uart输出分析一条龙, 我基本可以不用管, 花token就行).
bruin@lmde7 ~ $ ./run-3.8-q5.sh 0.00.035.069 W Setting 'enable_thinking' via --chat-template-kwargs is deprecated. Use --reasoning on / --reasoning off instead. 0.00.035.097 W DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead 0.00.039.005 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.039.491 W srv llama_server: ----------------- 0.00.039.495 W srv llama_server: CORS is set to allow all origins ('*') and no API key is set 0.00.039.495 W srv llama_server: this can be a security risk (cross-origin attacks) 0.00.039.495 W srv llama_server: more info: https://github.com/ggml-org/llama.cpp/pull/25655 0.00.039.495 W srv llama_server: ----------------- 0.00.040.765 I srv load_model: loading model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.08.836.391 I cmn init: llama threadpool init, n_threads = 10 0.09.247.607 I common_speculative_init_result: creating MTP draft context against the target model '/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf' 0.09.318.357 I srv load_model: initializing, n_slots = 1, n_ctx_slot = 131072, kv_unified = 'true' 0.09.457.688 I srv init: chat template supports preserving reasoning, consider enabling it via --reasoning-preserve 0.09.457.746 I srv llama_server: model loaded 0.09.457.750 I srv llama_server: listening on http://0.0.0.0:8000 0.31.502.907 I slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1 0.31.503.144 I slot launch_slot_: id 0 | task 0 | processing task, is_child = 0 0.33.674.917 I slot print_timing: id 0 | task 0 | prompt eval time = 1607.08 ms / 333 tokens ( 4.83 ms per token, 207.21 tokens per second) 0.33.674.922 I slot print_timing: id 0 | task 0 | eval time = 564.47 ms / 34 tokens ( 17.11 ms per token, 58.46 tokens per second) 0.33.674.923 I slot print_timing: id 0 | task 0 | total time = 2171.56 ms / 367 tokens 0.33.674.928 I slot print_timing: id 0 | task 0 | graphs reused = 11 0.33.674.930 I slot print_timing: id 0 | task 0 | draft acceptance = 0.72727 ( 24 accepted / 33 generated), mean len = 3.18 0.33.674.999 I slot release: id 0 | task 0 | stop processing: n_tokens = 368, truncated = 0 0.39.250.971 I slot get_availabl: id 0 | task -1 | selected slot by LCP similarity, f_sim_best = 0.963 (> 0.100 thold), f_keep = 1.000 0.39.251.208 I slot launch_slot_: id 0 | task 16 | processing task, is_child = 0 0.40.592.295 I slot print_timing: id 0 | task 16 | prompt eval time = 368.90 ms / 14 tokens ( 26.35 ms per token, 37.95 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | eval time = 972.01 ms / 67 tokens ( 14.73 ms per token, 67.90 tokens per second) 0.40.592.303 I slot print_timing: id 0 | task 16 | total time = 1340.91 ms / 81 tokens 0.40.592.305 I slot print_timing: id 0 | task 16 | graphs reused = 31 0.40.592.307 I slot print_timing: id 0 | task 16 | draft acceptance = 0.71429 ( 45 accepted / 63 generated), mean len = 3.14 0.40.592.367 I slot release: id 0 | task 16 | stop processing: n_tokens = 448, truncated = 0llama.cpp 我用的最新 b10485; 完全抄作业:
bruin@lmde7 ~ $ cat run-3.8-q5.sh #!/bin/bash # ref: https://lcz.me/topic/1157/qwen3.8-27b-q5_k_m-7900xtx-deepseek-harness%E5%AE%9E%E6%88%98%E5%B9%B3%E5%9D%87-52-t-s LLAMA_SERVER=/home/bruin/llama-server-vulkan-b10485 MAIN_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/Qwen3.8-27B-Q5_K_S.gguf" MTMD_MODEL="/opt/gguf-models/unsloth/Qwen3.8-27B-GGUF/mmproj-F16.gguf" #--mmproj "${MTMD_MODEL}" \ ${LLAMA_SERVER} \ --device Vulkan0 \ --model "${MAIN_MODEL}" \ -t 10 \ -b 512 \ -ub 256 \ --spec-draft-n-max 3 \ --fit off \ --no-context-shift \ --metrics \ --kv-unified \ --jinja \ --cache-type-k q8_0 \ --cache-type-v q8_0 \ -fa on \ --spec-type draft-mtp \ --ctx-size 131072 \ --parallel 1 \ -ngl -1 \ --host 0.0.0.0 \ --port 8000 \ --chat-template-kwargs '{"enable_thinking": true, "preserve_think": false, "reasoning_effort": "medium"}' \ --no-mmap \ --temp 0.6 \ --top-p 0.5 \ --top-k 15 \ --repeat-penalty 1.0 \ --override-tensor blk\.\d+\.ffn_.*_exps\.=CPU \ --alias qwen3.8-27b-q5@laobenxiong 确实，用了你这个q5ks的模型，我的速度终于正常了，也不用关thinking，不知道为什么其他人的q5km可以
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关于--parallel 2 的测试
软硬件环境：
cpu： AMD Ryzen 7 9700X
RAM：DDR5 64GB 6000
GPU：R9700 32G
OS： Ubuntu 24.04
软件 ： llama.cpp + vulkan结论：将并行设为1或者2，大部分情况下，性能变化属于正常波动，没有明显差别。
但是，即使有32G显存，有些任务还是会报以下错误：
26.48.401.777 E init_batch: failed to prepare attention ubatches
26.48.401.778 W decode: failed to find a memory slot for batch of size 16
26.48.401.779 W srv decode: failed to find free space in the KV cache, retrying with smaller batch size, off = 190, n_batch = 8, ret = 1
26.48.403.275 E init_batch: failed to prepare attention ubatches
26.48.403.277 W decode: failed to find a memory slot for batch of size 8
26.48.403.277 W srv decode: failed to find free space in the KV cache, retrying with smaller batch size, off = 190, n_batch = 4, ret = 1
虽然使用了 --cache-type-k/v q8_0 做 KV 量化来节省显存，但是 128K 的超长上下文，加上 --parallel 2，对 KV Cache 的要求依然极其恐怖。模型权重稳稳地占了 17GB。但当开启并发，且 Prompt 超长时，给 128K 上下文预留的 KV Cache 空间，在高峰期（特别是并发请求时）瞬间耗尽了，导致没有空位给下一个 Token 的生成。
好消息是，llama.cpp不能为KV Cache分配空间，并不会导致崩溃或任务中断（我没有碰到过），只会导致llama.cpp降级重试，严重的情况下会感觉明显卡顿而已，不用过于担心。
所以，用7900XTX的朋友，把并发设为1可能是更好的选择。 -
系统 于 取消固定此主题
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请问大佬测试过rocm么？我现在使用rocm搭配您的参数基本都是首 token 平均 5.8s · 36 tok/s的数据，想看看是个例还是都这么慢。
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7900xtx 全开模式下功耗多少
@Stone-Wong 西风卖的公版显卡默认上限291w，跑满大概280w
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我不懂， 我的 3090 24G 跑 q4km 的128K 上下文， 平均下来也就23tokens /s ， 为啥 xtx可以 50多
哦发现了， 我是跑一晚上， 楼主只跑了几分钟
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Y yi song 于 引用了 此主题
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7900xtx 全开模式下功耗多少
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@Stone-Wong 7500F 32G DDR5 1TB硬盘，7900XTX 满功率在492W。连续测试过3个小时功率就在480-492W
