{"id":32828,"date":"2026-09-01T17:18:40","date_gmt":"2026-09-01T09:18:40","guid":{"rendered":"https:\/\/jdssl.top\/?p=32828"},"modified":"2026-09-02T00:54:53","modified_gmt":"2026-09-01T16:54:53","slug":"5070ti16g-run-qwen-3-8-flash-next-iq4x-gguf","status":"publish","type":"post","link":"https:\/\/jdssl.top\/index.php\/2026\/09\/01\/5070ti16g-run-qwen-3-8-flash-next-iq4x-gguf\/","title":{"rendered":"5070ti16g\u5355\u5361\u8dd1qwen 3.8 flash next iq4x gguf Hermes\u8c03\u7528 18tokens\/s"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
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\u6211\u7684\u7535\u8111\u914d\u7f6e\uff1ai5 12\u4ee3\u5904\u7406\u5668\uff0c64gddr4 \uff0c5070ti16g\uff0c3070 8g,2tnvme \u9ad8\u901f\u786c\u76d8\u3002<\/p>

\u6a21\u578b1qwen3.8 iq4xs\uff1ahttps:\/\/huggingface.co\/AtomicChat\/Qwen3.8-Flash-Next-GGUF\/tree\/main\/Qwen3.8-Flash-Next-AD-3.84bpw-IQ4_XS-M64<\/a><\/p>

\u6a21\u578b2qwen3.8 iq4xs:https:\/\/huggingface.co\/orcarouter\/Qwen3.8-Flash-Next-Uncensored-GGUF\/tree\/main<\/a><\/p>

llama.cpp\u7248\u672c\uff1ahttps:\/\/github.com\/ggml-org\/llama.cpp\/releases\/tag\/b10718<\/a><\/p>

\u5355\u5361\u5b9e\u6d4b\u8f93\u51fa\u901f\u5ea6\u5c06\u8fd119t\/s<\/p>

Hermes\u8c03\u7528\u5728\u6211\u7684\u5e94\u7528\u573a\u666f\u5de5\u4f5c\u975e\u5e38\u6d41\u7545\uff0c\u5927\u90e8\u5206\u4efb\u52a1\u90fd\u80fd\u8f93\u4f60\u5b8c\u6210\u3002<\/p>

\u6ce8\u610f\uff1a\u9884\u70ed\u521d\u59cb\u6bd4\u8f83\u6162\uff0c\u9884\u70ed\u540e\uff0c\u4e0d\u8981\u7ecf\u5e38\u5173\u95ed\u6a21\u578b\u901f\u5ea6\u975e\u5e38\u7406\u60f3\u548c\u7a33\u5b9a\u3002<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
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\u4e0b\u65b9\u662fwin11\u4e0b \uff0cbat\u811a\u672c\u5206\u4eab\uff0c\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7txt\u6587\u6863\u7f16\u8f91\u4fee\u6539\u4e3abat\u811a\u672c<\/h5>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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@echo off
setlocal EnableExtensions
chcp 65001 >nul

rem ============================================================
rem Qwen3.8-Flash-Next Uncensored IQ4_XS
rem Windows + RTX 5070 Ti 16GB
rem llama.cpp b10718
rem Single GPU Auto-Fit Performance
rem Context: 131072 \/ 128K
rem No API Key Authentication
rem ============================================================

set \"SCRIPT_DIR=%~dp0\"
set \"STOP_SCRIPT=%SCRIPT_DIR%stop-llama-server.bat\"

set \"ROOT=C:\\Users\\Administrator\\llama.cpp\"
set \"LLAMA_SERVER=%ROOT%\\bin\\llama-server.exe\"

set \"MODEL_DIR=D:\\ai\\Models\\llama\\OrcaRouter-Qwen3.8-Flash-Next-Uncensored-IQ4_XS\"
set \"MODEL=%MODEL_DIR%\\Qwen3.8-Flash-Next-Uncensored-IQ4_XS-00001-of-00003.gguf\"

rem ============================================================
rem Network
rem ============================================================

set \"HOST=0.0.0.0\"
set \"PORT=8081\"
set \"LAN_IP=127.0.0.1\"

rem ============================================================
rem GPU \/ Performance tuning
rem ============================================================

rem RTX 5070 Ti
set \"GPU_DEVICE=CUDA0\"

rem Auto-Fit VRAM reserve
set \"FIT_TARGET=1536\"  ##\u8c03\u6574\u4e3a1024\u901f\u5ea6\u8fd8\u80fd\u63d0\u5347\uff0c\u4f46\u6ce8\u610f\u663e\u5b58\u5bb9\u91cf
rem Context
set \"CONTEXT_SIZE=131072\"

rem Batch settings
set \"BATCH_SIZE=1024\"
set \"UBATCH_SIZE=256\"

rem CPU thread settings
set \"CPU_THREADS=8\"
set \"CPU_BATCH_THREADS=12\"

rem ============================================================
rem Detect LAN IPv4
rem ============================================================

for \/f \"tokens=*\" %%i in ('powershell -NoProfile -Command \"$a=Get-NetIPAddress -AddressFamily IPv4; ($a.Where({$_.IPAddress -notlike '127.*' -and $_.PrefixOrigin -ne 'WellKnown'})[0].IPAddress)\"') do set \"LAN_IP=%%i\"

if \"%LAN_IP%\"==\"\" set \"LAN_IP=127.0.0.1\"

echo.
echo =================================================================
echo Qwen3.8 Flash Next Uncensored IQ4_XS - RTX 5070 Ti Auto-Fit
echo Context: 128K
echo Authentication: Disabled
echo =================================================================
echo.

rem ============================================================
rem 1\/4 Check files
rem ============================================================

echo [1\/4] Checking files...
echo.

if not exist \"%LLAMA_SERVER%\" (
echo ERROR: llama-server.exe not found:
echo %LLAMA_SERVER%
echo.
pause
exit \/b 1
)

if not exist \"%MODEL%\" (
echo ERROR: Model not found:
echo %MODEL%
echo.
pause
exit \/b 1
)

echo llama-server : OK
echo Model : OK
echo.

rem ============================================================
rem 2\/4 Stop existing llama.cpp processes
rem ============================================================

echo [2\/4] Stopping existing llama.cpp processes...
echo.

if exist \"%STOP_SCRIPT%\" (
call \"%STOP_SCRIPT%\" \/silent
) else (
taskkill \/F \/IM llama-server.exe >nul 2>nul
taskkill \/F \/IM llama-cli.exe >nul 2>nul
taskkill \/F \/IM llama-mtmd-cli.exe >nul 2>nul
)

%SystemRoot%\\System32\\WindowsPowerShell\\v1.0\\powershell.exe -NoProfile -Command \"Start-Sleep -Seconds 1\"

rem ============================================================
rem 3\/4 Show configuration
rem ============================================================

echo.
echo [3\/4] Configuration
echo.

echo -----------------------------------------------------------------
echo Network
echo -----------------------------------------------------------------
echo Bind : http:\/\/%HOST%:%PORT%
echo Local : http:\/\/127.0.0.1:%PORT%
echo LAN : http:\/\/%LAN_IP%:%PORT%
echo Auth : Disabled
echo.

echo -----------------------------------------------------------------
echo Model
echo -----------------------------------------------------------------
echo Model : Qwen3.8-Flash-Next Uncensored IQ4_XS
echo Model Size : 90.8 GiB
echo Context : %CONTEXT_SIZE% \/ 128K
echo Output Max : 4096
echo.

echo -----------------------------------------------------------------
echo GPU
echo -----------------------------------------------------------------
echo GPU : RTX 5070 Ti only
echo Device : %GPU_DEVICE%
echo RTX 3070 : Disabled
echo Split Mode : none
echo.

echo -----------------------------------------------------------------
echo Auto-Fit
echo -----------------------------------------------------------------
echo Auto Fit : ON
echo Fit Target : %FIT_TARGET% MiB
echo.

echo -----------------------------------------------------------------
echo Memory
echo -----------------------------------------------------------------
echo Load Mode : mmap
echo Lazy Mode : ON
echo KV Cache : Q4_0 \/ Q4_0
echo.

echo -----------------------------------------------------------------
echo Performance
echo -----------------------------------------------------------------
echo Batch : %BATCH_SIZE%
echo UBatch : %UBATCH_SIZE%
echo Threads : %CPU_THREADS%
echo Batch Thr : %CPU_BATCH_THREADS%
echo Flash Attn : ON
echo.

echo -----------------------------------------------------------------
echo Speculative Decoding
echo -----------------------------------------------------------------
echo Spec : OFF
echo.

echo -----------------------------------------------------------------
echo Reasoning
echo -----------------------------------------------------------------
echo Reasoning : auto
echo Budget : 2048
echo Format : deepseek
echo.

rem ============================================================
rem 4\/4 Start llama-server
rem ============================================================

echo [4\/4] Loading model...
echo.

echo =================================================================
echo Watch startup log for:
echo.
echo CUDA0 model buffer size
echo CPU model buffer size
echo KV buffer size
echo compute buffer size
echo offloaded layers
echo.
echo Server is ready when llama-server starts listening.
echo =================================================================
echo.

cd \/d \"%ROOT%\\bin\"

\"%LLAMA_SERVER%\" ^
-m \"%MODEL%\" ^
--host \"%HOST%\" ^
--port %PORT% ^
--alias \"qwen3.8-flash-next-uncensored-iq4_xs-5070ti-128k\" ^
--jinja ^
--reasoning auto ^
--reasoning-budget 2048 ^
--reasoning-format deepseek ^
-c %CONTEXT_SIZE% ^
-n 4096 ^
-b %BATCH_SIZE% ^
-ub %UBATCH_SIZE% ^
--parallel 1 ^
--device %GPU_DEVICE% ^
--split-mode none ^
--fit on ^
--fit-target %FIT_TARGET% ^
--load-mode mmap ^
--lazy-mode on ^
-ctk q4_0 ^
-ctv q4_0 ^
--flash-attn on ^
--threads %CPU_THREADS% ^
--threads-batch %CPU_BATCH_THREADS% ^
--cache-prompt ^
--log-colors off ^
--temp 1.0 ^
--top-p 0.95 ^
--top-k 20 ^
--min-p 0 ^
--presence-penalty 0.0 ^
--repeat-penalty 1.02

set \"ERR=%ERRORLEVEL%\"

echo.
echo =================================================================
echo [INFO] llama-server exited with code: %ERR%
echo =================================================================
echo.

pause
exit \/b %ERR%<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
\u4f7f\u7528\u524d\u8bf4\u660e\uff1a\u8fd0\u884c\u811a\u672c\u524d\uff0c\u8bf7\u5148\u4fee\u6539\u8fd9\u4e9b\u53c2\u6570
\u8fd9\u4efd\u542f\u52a8\u811a\u672c\u662f\u6309\u7167\u6211\u81ea\u5df1\u7684\u7535\u8111\u73af\u5883\u7f16\u5199\u7684\u3002
\u6211\u7684\u73af\u5883\u662f\uff1a
Windows
RTX 5070 Ti 16GB
llama.cpp b10718
Qwen3.8-Flash-Next IQ4_XS
Hermes Agent
\u56e0\u6b64\uff0c\u5927\u5bb6\u590d\u5236\u811a\u672c\u4ee5\u540e\uff0c\u4e0d\u5efa\u8bae\u76f4\u63a5\u53cc\u51fb\u8fd0\u884c\u3002
\u4e0d\u540c\u7535\u8111\u7684 llama.cpp \u5b89\u88c5\u4f4d\u7f6e\u3001GGUF \u6a21\u578b\u8def\u5f84\u3001GPU \u7f16\u53f7\u3001\u7cfb\u7edf\u5185\u5b58\u548c\u663e\u5b58\u60c5\u51b5\u90fd\u53ef\u80fd\u4e0d\u540c\u3002
\u7b2c\u4e00\u6b21\u4f7f\u7528\u65f6\uff0c\u6700\u91cd\u8981\u7684\u662f\u5148\u68c0\u67e5\u4e0b\u9762 4 \u4e2a\u53c2\u6570\uff1a
ROOT
MODEL_DIR
MODEL
GPU_DEVICE
\u5176\u4e2d\u524d 3 \u4e2a\u53c2\u6570\u51e0\u4e4e\u6bcf\u4e2a\u4eba\u90fd\u9700\u8981\u6839\u636e\u81ea\u5df1\u7684\u7535\u8111\u4fee\u6539\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e00\u3001\u4fee\u6539 llama.cpp \u5b89\u88c5\u8def\u5f84
\u811a\u672c\u9ed8\u8ba4\u5199\u7684\u662f\uff1a
set “ROOT=C:\\Users\\Administrator\\llama.cpp”
\u8fd9\u4e2a\u53c2\u6570\u4ee3\u8868 llama.cpp \u7684\u5b89\u88c5\u76ee\u5f55\u3002
\u4e5f\u5c31\u662f\u8bf4\uff0c\u6211\u81ea\u5df1\u7684 llama.cpp \u4f4d\u4e8e\uff1a
C:\\Users\\Administrator\\llama.cpp
\u5982\u679c\u4f60\u7684 llama.cpp \u5b89\u88c5\u5728\u5176\u4ed6\u4f4d\u7f6e\uff0c\u5c31\u9700\u8981\u4fee\u6539\u3002
\u4f8b\u5982\u4f60\u7684 llama.cpp \u4f4d\u4e8e\uff1a
D:\\llama.cpp
\u90a3\u4e48\u4fee\u6539\u4e3a\uff1a
set “ROOT=D:\\llama.cpp”
\u5982\u679c\u4f4d\u4e8e\uff1a
C:\\AI\\llama.cpp
\u90a3\u4e48\u4fee\u6539\u4e3a\uff1a
set “ROOT=C:\\AI\\llama.cpp”
\u811a\u672c\u4e2d\u8fd8\u6709\u8fd9\u4e00\u884c\uff1a
set “LLAMA_SERVER=%ROOT%\\bin\\llama-server.exe”
\u8fd9\u4e00\u884c\u901a\u5e38\u4e0d\u9700\u8981\u4fee\u6539\u3002
\u56e0\u4e3a\u5b83\u4f1a\u81ea\u52a8\u6839\u636e ROOT \u7684\u4f4d\u7f6e\u5bfb\u627e\uff1a
bin\\llama-server.exe
\u6240\u4ee5\u53ea\u9700\u8981\u4fdd\u8bc1 ROOT \u586b\u5199\u6b63\u786e\u5373\u53ef\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u3001\u4fee\u6539 GGUF \u6a21\u578b\u76ee\u5f55
\u811a\u672c\u9ed8\u8ba4\u6a21\u578b\u76ee\u5f55\u662f\uff1a
set “MODEL_DIR=D:\\ai\\Models\\llama\\OrcaRouter-Qwen3.8-Flash-Next-Uncensored-IQ4_XS”
\u8fd9\u4e2a\u8def\u5f84\u662f\u6211\u81ea\u5df1\u7535\u8111\u4e0a\u7684\u6a21\u578b\u4fdd\u5b58\u4f4d\u7f6e\u3002
\u5927\u5bb6\u81ea\u5df1\u7684\u6a21\u578b\u8def\u5f84\u4e00\u822c\u90fd\u4f1a\u4e0d\u540c\uff0c\u6240\u4ee5\u8fd9\u91cc\u57fa\u672c\u90fd\u9700\u8981\u4fee\u6539\u3002
\u4f8b\u5982\u4f60\u7684\u6a21\u578b\u653e\u5728\uff1a
E:\\AI\\Models\\Qwen3.8-Flash-Next
\u90a3\u4e48\u4fee\u6539\u4e3a\uff1a
set “MODEL_DIR=E:\\AI\\Models\\Qwen3.8-Flash-Next”
\u8fd9\u91cc\u586b\u5199\u7684\u662f GGUF \u6a21\u578b\u6240\u5728\u7684\u6587\u4ef6\u5939\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e09\u3001\u4fee\u6539 GGUF \u6a21\u578b\u6587\u4ef6\u540d
\u811a\u672c\u4e2d\u9ed8\u8ba4\u662f\uff1a
set “MODEL=%MODEL_DIR%\\Qwen3.8-Flash-Next-Uncensored-IQ4_XS-00001-of-00003.gguf”
\u8fd9\u4e00\u884c\u4ee3\u8868\u771f\u6b63\u8981\u52a0\u8f7d\u7684 GGUF \u6a21\u578b\u6587\u4ef6\u3002
\u4e0d\u540c\u4f5c\u8005\u53d1\u5e03\u7684\u6a21\u578b\u3001\u4e0d\u540c\u91cf\u5316\u7248\u672c\uff0c\u6587\u4ef6\u540d\u90fd\u4f1a\u6709\u6240\u4e0d\u540c\u3002
\u6240\u4ee5\u5927\u5bb6\u9700\u8981\u8fdb\u5165\u81ea\u5df1\u7684\u6a21\u578b\u76ee\u5f55\uff0c\u627e\u5230\u5b9e\u9645\u4e0b\u8f7d\u7684 GGUF \u6587\u4ef6\u540d\u3002
\u4f8b\u5982\u4f60\u7684\u6a21\u578b\u6587\u4ef6\u53eb\uff1a
Qwen3.8-Flash-Next-AD-3.84bpw-IQ4_XS-M64-00001-of-00003.gguf
\u90a3\u4e48\u4fee\u6539\u4e3a\uff1a
set “MODEL=%MODEL_DIR%\\Qwen3.8-Flash-Next-AD-3.84bpw-IQ4_XS-M64-00001-of-00003.gguf”
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u56db\u3001\u591a\u5206\u7247 GGUF \u600e\u4e48\u586b\u5199\uff1f
\u8fd9\u79cd\u5927\u578b\u6a21\u578b\u901a\u5e38\u4f1a\u88ab\u62c6\u6210\u591a\u4e2a GGUF \u6587\u4ef6\u3002
\u4f8b\u5982\uff1a
Qwen3.8-Flash-Next-IQ4_XS-00001-of-00003.gguf
Qwen3.8-Flash-Next-IQ4_XS-00002-of-00003.gguf
Qwen3.8-Flash-Next-IQ4_XS-00003-of-00003.gguf
\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u811a\u672c\u91cc\u9762\u53ea\u9700\u8981\u6307\u5b9a\u7b2c\u4e00\u7247\uff1a
00001-of-00003.gguf
\u4f8b\u5982\uff1a
set “MODEL=%MODEL_DIR%\\Qwen3.8-Flash-Next-IQ4_XS-00001-of-00003.gguf”
\u6b63\u5e38\u60c5\u51b5\u4e0b\uff0cllama.cpp \u4f1a\u81ea\u52a8\u8bfb\u53d6\u540e\u9762\u7684\uff1a
00002-of-00003.gguf
00003-of-00003.gguf
\u4e0d\u9700\u8981\u628a\u4e09\u4e2a\u6587\u4ef6\u5206\u522b\u5199\u8fdb\u811a\u672c\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e94\u3001\u68c0\u67e5 GPU_DEVICE
\u811a\u672c\u9ed8\u8ba4\uff1a
set “GPU_DEVICE=CUDA0”
\u5b83\u4ee3\u8868\u4f7f\u7528 llama.cpp \u8bc6\u522b\u5230\u7684 CUDA0 \u663e\u5361\u3002
\u5982\u679c\u4f60\u7684\u7535\u8111\u53ea\u6709\u4e00\u5f20 NVIDIA \u663e\u5361\uff0c\u4e00\u822c\u4fdd\u6301 CUDA0 \u5c31\u53ef\u4ee5\u3002
\u4f46\u5982\u679c\u4f60\u7684\u7535\u8111\u91cc\u9762\u6709\u4e24\u5f20\u6216\u66f4\u591a NVIDIA \u663e\u5361\uff0c\u5c31\u4e00\u5b9a\u8981\u68c0\u67e5\u3002
\u4f8b\u5982\u4f60\u7684\u7535\u8111\u6709\uff1a
RTX 5070 Ti
RTX 3070
llama.cpp \u53ef\u80fd\u8bc6\u522b\u4e3a\uff1a
CUDA0 = RTX 5070 Ti
CUDA1 = RTX 3070
\u8fd9\u79cd\u60c5\u51b5\u4e0b\u4fdd\u6301\uff1a
set “GPU_DEVICE=CUDA0”
\u5c31\u53ef\u4ee5\u3002
\u4f46\u662f\u4e5f\u6709\u53ef\u80fd\u53d8\u6210\uff1a
CUDA0 = RTX 3070
CUDA1 = RTX 5070 Ti
\u90a3\u4e48\u5c31\u9700\u8981\u4fee\u6539\u4e3a\uff1a
set “GPU_DEVICE=CUDA1”
\u6240\u4ee5\u591a\u663e\u5361\u7528\u6237\u4e0d\u8981\u9ed8\u8ba4\u8ba4\u4e3a CUDA0 \u4e00\u5b9a\u5c31\u662f\u6027\u80fd\u6700\u5f3a\u7684\u90a3\u5f20\u663e\u5361\u3002
\u5efa\u8bae\u542f\u52a8 llama.cpp \u540e\u67e5\u770b\u524d\u9762\u7684 CUDA \u521d\u59cb\u5316\u65e5\u5fd7\uff0c\u786e\u8ba4\u663e\u5361\u5bf9\u5e94\u7f16\u53f7\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u516d\u3001\u4e3a\u4ec0\u4e48\u811a\u672c\u4f7f\u7528 –split-mode none\uff1f
\u811a\u672c\u4f7f\u7528\uff1a
–device %GPU_DEVICE%
–split-mode none
\u76ee\u7684\u5c31\u662f\u53ea\u4f7f\u7528\u6307\u5b9a\u7684\u4e00\u5f20\u663e\u5361\u3002
\u4f8b\u5982\uff1a
set “GPU_DEVICE=CUDA0”
\u90a3\u4e48\u6a21\u578b\u5c31\u53ea\u4f7f\u7528 CUDA0\u3002
\u5373\u4f7f\u7535\u8111\u91cc\u8fd8\u6709 RTX 3070\u3001RTX 3060 \u6216\u5176\u4ed6\u663e\u5361\uff0c\u4e5f\u4e0d\u4f1a\u81ea\u52a8\u52a0\u5165\u6a21\u578b\u5206\u5361\u3002
\u8fd9\u4efd\u914d\u7f6e\u4e3b\u8981\u9488\u5bf9\uff1a
RTX 5070 Ti 16GB \u5355\u5361\u8fd0\u884c
\u76ee\u6807\u5e76\u4e0d\u662f\u8ffd\u6c42\u628a\u6a21\u578b\u5206\u6563\u5230\u66f4\u591a\u663e\u5361\uff0c\u800c\u662f\u4f18\u5148\u4fdd\u8bc1\u5355\u7528\u6237 Hermes \u4f7f\u7528\u65f6\u7684\u751f\u6210\u901f\u5ea6\u548c\u7a33\u5b9a\u6027\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e03\u3001\u7aef\u53e3 PORT \u662f\u5426\u9700\u8981\u4fee\u6539\uff1f
\u9ed8\u8ba4\uff1a
set “PORT=8081”
\u542f\u52a8\u4ee5\u540e\uff0c\u672c\u673a\u5730\u5740\u662f\uff1a
http:\/\/127.0.0.1:8081
\u5982\u679c\u4f7f\u7528 OpenAI Compatible API\uff0c\u4e00\u822c\u586b\u5199\uff1a
http:\/\/127.0.0.1:8081\/v1
\u5982\u679c 8081 \u6ca1\u6709\u88ab\u5176\u4ed6\u7a0b\u5e8f\u5360\u7528\uff0c\u5c31\u4e0d\u9700\u8981\u4fee\u6539\u3002
\u5982\u679c\u63d0\u793a\u7aef\u53e3\u88ab\u5360\u7528\uff0c\u53ef\u4ee5\u4fee\u6539\u6210\uff1a
set “PORT=8082”
\u6216\u8005\uff1a
set “PORT=8000”
\u9700\u8981\u6ce8\u610f\uff1a
\u4fee\u6539 llama.cpp \u7684\u7aef\u53e3\u4ee5\u540e\uff0cHermes \u91cc\u9762\u7684 API \u5730\u5740\u4e5f\u8981\u4e00\u8d77\u4fee\u6539\u3002
\u4f8b\u5982\u7aef\u53e3\u6539\u6210 8082\uff0c\u90a3\u4e48 Hermes \u5e94\u8be5\u8fde\u63a5\uff1a
http:\/\/127.0.0.1:8082\/v1
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u516b\u3001Context \u4e0a\u4e0b\u6587\u957f\u5ea6\u600e\u4e48\u9009\u62e9\uff1f
\u811a\u672c\u9ed8\u8ba4\uff1a
set “CONTEXT_SIZE=131072”
\u4e5f\u5c31\u662f\uff1a
131072 Tokens
\u7ea6\u7b49\u4e8e 128K Context\u3002
\u6211\u81ea\u5df1\u4e3b\u8981\u4f7f\u7528 Hermes Agent\uff0c\u6240\u4ee5\u8bbe\u7f6e\u6210\u4e86 128K\u3002
\u8f83\u5927\u7684 Context \u53ef\u4ee5\u5bb9\u7eb3\u66f4\u591a\uff1a
\u5bf9\u8bdd\u8bb0\u5f55
System Prompt
Agent Prompt
Tool \u5b9a\u4e49
\u7f51\u9875\u5185\u5bb9
\u4ee3\u7801
Terminal \u8f93\u51fa
\u5de5\u5177\u8c03\u7528\u8bb0\u5f55
\u4f46\u662f Context \u8d8a\u5927\uff0c\u5bf9\u5185\u5b58\u548c\u7f13\u5b58\u8d44\u6e90\u7684\u8981\u6c42\u4e5f\u4f1a\u8d8a\u9ad8\u3002
\u6240\u4ee5\u5e76\u4e0d\u662f\u6240\u6709\u7528\u6237\u90fd\u5fc5\u987b\u4f7f\u7528 128K\u3002
\u5982\u679c\u7cfb\u7edf\u5185\u5b58\u6bd4\u8f83\u7d27\u5f20\uff0c\u53ef\u4ee5\u5148\u6539\u6210 64K\uff1a
set “CONTEXT_SIZE=65536”
\u5982\u679c\u8fd8\u60f3\u8fdb\u4e00\u6b65\u964d\u4f4e\uff0c\u53ef\u4ee5\u4f7f\u7528 32K\uff1a
set “CONTEXT_SIZE=32768”
\u5982\u679c\u53ea\u662f\u666e\u901a\u804a\u5929\u3001\u5199\u4ee3\u7801\u6216\u8005\u6d4b\u8bd5\u6a21\u578b\uff0c32K\uff5e64K \u901a\u5e38\u5df2\u7ecf\u591f\u7528\u3002
\u5982\u679c\u4e3b\u8981\u8fd0\u884c Hermes Agent\uff0c\u800c\u4e14\u5185\u5b58\u6bd4\u8f83\u5145\u8db3\uff0c\u518d\u8003\u8651\u4f7f\u7528 128K\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e5d\u3001FIT_TARGET \u662f\u4ec0\u4e48\uff1f
\u811a\u672c\u9ed8\u8ba4\uff1a
set “FIT_TARGET=1536”
\u5b9e\u9645\u542f\u52a8\u65f6\u5bf9\u5e94\uff1a
–fit on
–fit-target 1536
\u7b80\u5355\u7406\u89e3\u5c31\u662f\uff1a
\u8ba9 llama.cpp \u81ea\u52a8\u51b3\u5b9a\u600e\u6837\u5229\u7528\u663e\u5b58\uff0c\u540c\u65f6\u76ee\u6807\u4fdd\u7559\u5927\u7ea6 1536 MiB \u7684\u7a7a\u95f2\u663e\u5b58\u3002
1536 MiB \u5927\u7ea6\u5c31\u662f 1.5GB\u3002
\u5bf9\u4e8e RTX 5070 Ti 16GB\uff0c\u6211\u6ca1\u6709\u8ba9 llama.cpp \u628a 16GB \u663e\u5b58\u5168\u90e8\u585e\u6ee1\u3002
\u6545\u610f\u7559\u4e0b\u4e00\u90e8\u5206\u663e\u5b58\uff0c\u662f\u4e3a\u4e86\u7ed9\u4e0b\u9762\u8fd9\u4e9b\u5185\u5bb9\u7559\u4e0b\u4f59\u91cf\uff1a
KV Cache
CUDA Compute Buffer
Context
CUDA \u4e34\u65f6\u8ba1\u7b97
Windows \u56fe\u5f62\u754c\u9762
\u5176\u4ed6\u7a0b\u5e8f\u7684\u663e\u5b58\u4f7f\u7528
\u6a21\u578b\u521a\u52a0\u8f7d\u6210\u529f\uff0c\u5e76\u4e0d\u4ee3\u8868\u540e\u9762\u751f\u6210\u5185\u5bb9\u65f6\u4e00\u5b9a\u4e0d\u4f1a\u7206\u663e\u5b58\u3002
\u6240\u4ee5\u9002\u5f53\u4fdd\u7559\u663e\u5b58\u53cd\u800c\u4f1a\u66f4\u52a0\u7a33\u5b9a\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u3001\u51fa\u73b0 CUDA Out of Memory \u600e\u4e48\u529e\uff1f
\u5982\u679c\u8fd0\u884c\u8fc7\u7a0b\u4e2d\u51fa\u73b0\uff1a
CUDA out of memory
\u53ef\u4ee5\u4f18\u5148\u63d0\u9ad8 FIT_TARGET\u3002
\u4f8b\u5982\uff1a
set “FIT_TARGET=2048”
\u8fd9\u6837\u4f1a\u76ee\u6807\u4fdd\u7559\u5927\u7ea6 2GB \u663e\u5b58\u3002
\u5982\u679c\u8fd8\u662f\u4e0d\u7a33\u5b9a\uff0c\u53ef\u4ee5\u7ee7\u7eed\u63d0\u9ad8\uff1a
set “FIT_TARGET=2560”
\u5982\u679c\u6a21\u578b\u8fd0\u884c\u540e\u53d1\u73b0\u8fd8\u6709\u5f88\u591a\u663e\u5b58\u6ca1\u6709\u4f7f\u7528\uff0c\u4e5f\u53ef\u4ee5\u5c1d\u8bd5\u964d\u4f4e\uff0c\u4f8b\u5982\uff1a
set “FIT_TARGET=1024”
\u8fd9\u6837 llama.cpp \u4f1a\u66f4\u52a0\u79ef\u6781\u5730\u4f7f\u7528 GPU \u663e\u5b58\u3002
\u4e0d\u8fc7\u4e0d\u5efa\u8bae\u4e3a\u4e86\u8ffd\u6c42\u663e\u5b58\u5360\u7528\u7387\uff0c\u628a\u663e\u5b58\u786c\u585e\u5230\u63a5\u8fd1 100%\u3002
\u5bf9\u4e8e\u672c\u5730\u5927\u6a21\u578b\u6765\u8bf4\uff0c\u7a33\u5b9a\u8fd0\u884c\u901a\u5e38\u6bd4\u591a Offload \u4e00\u70b9\u70b9\u66f4\u91cd\u8981\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u4e00\u3001CPU_THREADS \u662f\u5426\u9700\u8981\u4fee\u6539\uff1f
\u811a\u672c\u9ed8\u8ba4\uff1a
set “CPU_THREADS=8”
set “CPU_BATCH_THREADS=12”
\u8fd9\u4e24\u4e2a\u53c2\u6570\u8ddf\u7535\u8111 CPU \u6709\u5173\u7cfb\u3002
CPU_THREADS \u4e3b\u8981\u63a7\u5236\u666e\u901a CPU \u63a8\u7406\u7ebf\u7a0b\u3002
CPU_BATCH_THREADS \u66f4\u591a\u5f71\u54cd Prompt Processing \u548c Batch \u9636\u6bb5\u4f7f\u7528\u7684 CPU \u7ebf\u7a0b\u3002
\u4e0d\u540c CPU \u7684\u6700\u4f18\u8bbe\u7f6e\u5e76\u4e0d\u4e00\u6837\u3002
\u5982\u679c\u4e0d\u77e5\u9053\u5e94\u8be5\u600e\u4e48\u8bbe\u7f6e\uff0c\u53ef\u4ee5\u5148\u4fdd\u6301\uff1a
set “CPU_THREADS=8”
set “CPU_BATCH_THREADS=12”
\u7136\u540e\u89c2\u5bdf CPU \u5360\u7528\u548c\u5b9e\u9645\u8fd0\u884c\u901f\u5ea6\u3002
CPU \u6838\u5fc3\u6bd4\u8f83\u5c11\uff0c\u53ef\u4ee5\u9002\u5f53\u964d\u4f4e\u3002
CPU \u6838\u5fc3\u6bd4\u8f83\u591a\uff0c\u4e5f\u53ef\u4ee5\u81ea\u5df1\u6d4b\u8bd5\u63d0\u9ad8\u3002
\u4f46\u662f\u4e0d\u5efa\u8bae\u4e00\u5f00\u59cb\u5c31\u628a\u6240\u6709\u903b\u8f91\u7ebf\u7a0b\u5168\u90e8\u5360\u6ee1\u3002
Windows\u3001Hermes \u548c\u5176\u4ed6\u540e\u53f0\u7a0b\u5e8f\u540c\u6837\u9700\u8981\u4f7f\u7528 CPU\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u4e8c\u3001BATCH_SIZE \u548c UBATCH_SIZE \u662f\u5426\u9700\u8981\u4fee\u6539\uff1f
\u811a\u672c\u9ed8\u8ba4\uff1a
set “BATCH_SIZE=1024”
set “UBATCH_SIZE=256”
\u4e5f\u5c31\u662f\uff1a
Batch Size = 1024
UBatch Size = 256
\u8fd9\u4e24\u4e2a\u53c2\u6570\u4e3b\u8981\u5f71\u54cd\uff1a
Prompt Processing
Prefill
\u663e\u5b58\u4f7f\u7528
\u5b83\u4eec\u5e76\u4e0d\u662f\u7b80\u5355\u7684\u201c\u6570\u5b57\u8d8a\u5927\uff0cToken \u751f\u6210\u901f\u5ea6\u5c31\u8d8a\u5feb\u201d\u3002
\u5bf9\u4e8e RTX 5070 Ti 16GB\uff0c\u53ef\u4ee5\u5148\u4fdd\u6301\uff1a
1024 \/ 256
\u5982\u679c\u51fa\u73b0\u663e\u5b58\u4e0d\u8db3\uff0c\u53ef\u4ee5\u964d\u4f4e\u4e3a\uff1a
set “BATCH_SIZE=512”
set “UBATCH_SIZE=128”
\u5982\u679c\u9ed8\u8ba4\u8bbe\u7f6e\u53ef\u4ee5\u7a33\u5b9a\u8fd0\u884c\uff0c\u5c31\u6ca1\u6709\u5fc5\u8981\u7ee7\u7eed\u4e71\u6539\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u4e09\u3001\u4e3a\u4ec0\u4e48 KV Cache \u4f7f\u7528 Q4_0\uff1f
\u811a\u672c\u4e2d\u6709\uff1a
-ctk q4_0
-ctv q4_0
\u5206\u522b\u4ee3\u8868\uff1a
K Cache = Q4_0
V Cache = Q4_0
\u8fd9\u4e48\u505a\u4e3b\u8981\u662f\u4e3a\u4e86\u964d\u4f4e\u957f Context \u4e0b KV Cache \u7684\u5185\u5b58\u548c\u663e\u5b58\u538b\u529b\u3002
\u7279\u522b\u662f\u5728\u4f7f\u7528 128K Context \u65f6\uff0c\u5982\u679c KV Cache \u4f7f\u7528\u66f4\u9ad8\u7cbe\u5ea6\u683c\u5f0f\uff0c\u7f13\u5b58\u5360\u7528\u4f1a\u66f4\u5927\u3002
\u6240\u4ee5\u6211\u8fd9\u91cc\u4f7f\u7528 Q4_0\uff0c\u662f\u4e3a\u4e86\u5728\u4e0b\u9762\u51e0\u4e2a\u65b9\u9762\u53d6\u5f97\u5e73\u8861\uff1a
Context \u957f\u5ea6
\u5185\u5b58\u5360\u7528
\u663e\u5b58\u5360\u7528
\u5b9e\u9645\u4f7f\u7528\u4f53\u9a8c
\u666e\u901a\u7528\u6237\u5efa\u8bae\u5148\u4fdd\u6301\u9ed8\u8ba4\uff0c\u4e0d\u9700\u8981\u4fee\u6539\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u56db\u3001–lazy-mode on \u5efa\u8bae\u4fdd\u7559
\u811a\u672c\u4f7f\u7528\uff1a
–load-mode mmap
–lazy-mode on
\u5bf9\u4e8e Qwen3.8-Flash-Next \u8fd9\u79cd\u5927\u578b GGUF\uff0c\u8fd9\u4e24\u4e2a\u53c2\u6570\u6bd4\u8f83\u91cd\u8981\u3002
\u7b80\u5355\u7406\u89e3\uff1a
\u4e0d\u662f\u8bd5\u56fe\u628a\u6574\u4e2a\u8d85\u5927\u6a21\u578b\u5168\u90e8\u4e00\u6b21\u6027\u585e\u5165\u663e\u5b58\uff0c\u800c\u662f\u7ed3\u5408 mmap \u548c Lazy Loading\uff0c\u5bf9\u90e8\u5206\u5927\u578b Tensor \u8fdb\u884c\u66f4\u52a0\u5408\u7406\u7684\u6309\u9700\u8bbf\u95ee\u3002
\u6240\u4ee5\u5982\u679c\u4f7f\u7528\u7684\u5c31\u662f Qwen3.8-Flash-Next GGUF\uff0c\u5efa\u8bae\u5148\u4fdd\u6301\uff1a
–load-mode mmap
–lazy-mode on
\u4e0d\u8981\u968f\u4fbf\u5220\u9664\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u4e94\u3001Flash Attention \u5efa\u8bae\u4fdd\u6301\u5f00\u542f
\u811a\u672c\u4f7f\u7528\uff1a
–flash-attn on
\u5bf9\u4e8e\u957f\u4e0a\u4e0b\u6587\u6a21\u578b\uff0cFlash Attention \u53ef\u4ee5\u964d\u4f4e Attention \u9636\u6bb5\u7684\u4e00\u90e8\u5206\u5185\u5b58\u538b\u529b\uff0c\u5e76\u63d0\u9ad8\u8fd0\u884c\u6548\u7387\u3002
\u56e0\u6b64\u5728\u5f53\u524d\u652f\u6301\u8be5\u529f\u80fd\u7684 llama.cpp \u7248\u672c\u4e2d\uff0c\u5efa\u8bae\u4fdd\u6301\u5f00\u542f\u3002
\u5982\u679c\u542f\u52a8\u65f6\u63d0\u793a\uff1a
unknown argument
\u6216\u8005\u4e0d\u8bc6\u522b\uff1a
–flash-attn
\u4e00\u822c\u8bf4\u660e llama.cpp \u7248\u672c\u53ef\u80fd\u592a\u65e7\uff0c\u9700\u8981\u5347\u7ea7\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u516d\u3001\u4e3a\u4ec0\u4e48\u4f7f\u7528 –parallel 1\uff1f
\u811a\u672c\u9ed8\u8ba4\uff1a
–parallel 1
\u8fd9\u5957\u914d\u7f6e\u7684\u4e3b\u8981\u4f7f\u7528\u573a\u666f\u662f\uff1a
\u4e00\u4e2a\u7528\u6237
\u4e00\u4e2a Hermes Agent
\u4e00\u4e2a\u672c\u5730\u6a21\u578b\u670d\u52a1
\u56e0\u6b64\u6ca1\u6709\u5fc5\u8981\u4e3a\u4e86\u5e76\u53d1\uff0c\u628a Server \u5f00\u6210\u5f88\u591a\u4e2a Slot\u3002
\u5982\u679c\u53ea\u662f\u81ea\u5df1\u4f7f\u7528\uff1a
parallel = 1
\u5c31\u5df2\u7ecf\u8db3\u591f\u3002
\u5982\u679c\u4ee5\u540e\u9700\u8981\u591a\u4eba\u540c\u65f6\u8c03\u7528\uff0c\u6216\u8005\u591a\u53f0\u8bbe\u5907\u540c\u65f6\u8bf7\u6c42\u6a21\u578b\uff0c\u518d\u8003\u8651\u63d0\u9ad8 parallel\u3002
\u4f46\u662f Parallel \u589e\u52a0\u4ee5\u540e\uff0c\u4e5f\u4f1a\u589e\u52a0\uff1a
KV Cache \u5360\u7528
Context \u8d44\u6e90
\u7cfb\u7edf\u5185\u5b58\u538b\u529b
\u663e\u5b58\u538b\u529b
\u6240\u4ee5\u4e0d\u662f\u6570\u503c\u8d8a\u5927\u8d8a\u597d\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u4e03\u3001\u6ca1\u6709 stop-llama-server.bat \u600e\u4e48\u529e\uff1f
\u811a\u672c\u524d\u9762\u6709\uff1a
set “STOP_SCRIPT=%SCRIPT_DIR%stop-llama-server.bat”
\u6709\u4e9b\u7528\u6237\u53ef\u80fd\u4f1a\u53d1\u73b0\u81ea\u5df1\u7684\u76ee\u5f55\u91cc\u9762\u6ca1\u6709\u8fd9\u4e2a\u6587\u4ef6\u3002
\u8fd9\u4e2a\u6ca1\u6709\u5173\u7cfb\u3002
\u56e0\u4e3a\u811a\u672c\u5df2\u7ecf\u505a\u4e86\u81ea\u52a8\u5224\u65ad\u3002
\u5982\u679c\u5b58\u5728 stop-llama-server.bat\uff0c\u5c31\u8c03\u7528\u8fd9\u4e2a\u6587\u4ef6\u3002
\u5982\u679c\u4e0d\u5b58\u5728\uff0c\u5c31\u4f1a\u81ea\u52a8\u6267\u884c taskkill\uff0c\u5173\u95ed\u5df2\u6709\u7684 llama.cpp \u76f8\u5173\u8fdb\u7a0b\u3002
\u56e0\u6b64\u666e\u901a\u7528\u6237\u4e0d\u9700\u8981\u989d\u5916\u521b\u5efa stop-llama-server.bat\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u516b\u3001\u8fd9\u4e2a\u516c\u5f00\u811a\u672c\u6ca1\u6709 API Key
\u535a\u5ba2\u516c\u5f00\u7248\u672c\u5df2\u7ecf\u53bb\u6389\u4e86\uff1a
–api-key
\u4ee5\u53ca\uff1a
–api-key-file
\u6240\u4ee5\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u8bbf\u95ee llama-server \u4e0d\u9700\u8981\u586b\u5199 API Key\u3002
Hermes \u672c\u673a\u53ef\u4ee5\u76f4\u63a5\u8fde\u63a5\uff1a
http:\/\/127.0.0.1:8081\/v1
\u8fd9\u6837\u914d\u7f6e\u8d77\u6765\u66f4\u52a0\u7b80\u5355\u3002
\u4f46\u662f\u8fd9\u91cc\u6709\u4e00\u4e2a\u5b89\u5168\u95ee\u9898\u5fc5\u987b\u6ce8\u610f\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u5341\u4e5d\u3001HOST=0.0.0.0 \u7684\u5b89\u5168\u95ee\u9898
\u811a\u672c\u9ed8\u8ba4\uff1a
set “HOST=0.0.0.0”
0.0.0.0 \u7684\u610f\u601d\u4e0d\u662f\u53ea\u5141\u8bb8\u672c\u673a\u8bbf\u95ee\u3002
\u5b83\u4ee3\u8868 llama-server \u76d1\u542c\u5f53\u524d\u7535\u8111\u7684\u6240\u6709\u7f51\u7edc\u63a5\u53e3\u3002
\u4f8b\u5982\u4f60\u7684\u7535\u8111\u5c40\u57df\u7f51 IP \u662f\uff1a
192.168.1.100
\u90a3\u4e48\u540c\u4e00\u5c40\u57df\u7f51\u7684\u5176\u4ed6\u8bbe\u5907\u53ef\u80fd\u53ef\u4ee5\u901a\u8fc7\uff1a
http:\/\/192.168.1.100:8081
\u8bbf\u95ee\u4f60\u7684\u6a21\u578b\u3002
\u5982\u679c\u811a\u672c\u53c8\u6ca1\u6709\u8bbe\u7f6e API Key\uff0c\u90a3\u4e48\u4efb\u4f55\u80fd\u591f\u8bbf\u95ee\u8fd9\u4e2a\u7aef\u53e3\u7684\u8bbe\u5907\uff0c\u90fd\u53ef\u80fd\u8c03\u7528\u6a21\u578b\u3002
\u6240\u4ee5\uff1a
\u4e0d\u8981\u5728\u6ca1\u6709\u9274\u6743\u3001\u9632\u706b\u5899\u6216\u53cd\u5411\u4ee3\u7406\u4fdd\u62a4\u7684\u60c5\u51b5\u4e0b\uff0c\u628a 8081 \u7aef\u53e3\u76f4\u63a5\u66b4\u9732\u5230\u516c\u7f51\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u5341\u3001\u5982\u679c\u53ea\u8ba9\u672c\u673a Hermes \u4f7f\u7528
\u5982\u679c\u4f60\u53ea\u5728\u8fd9\u4e00\u53f0\u7535\u8111\u4e0a\u8fd0\u884c Hermes\uff0c\u5efa\u8bae\u628a\uff1a
set “HOST=0.0.0.0”
\u6539\u6210\uff1a
set “HOST=127.0.0.1”
\u8fd9\u6837 llama-server \u5c31\u53ea\u63a5\u53d7\u672c\u673a\u8bbf\u95ee\u3002
Hermes \u4ecd\u7136\u8fde\u63a5\uff1a
http:\/\/127.0.0.1:8081\/v1
\u5373\u53ef\u3002
\u5982\u679c\u4f60\u9700\u8981\u624b\u673a\u3001\u7b14\u8bb0\u672c\u6216\u5c40\u57df\u7f51\u5176\u4ed6\u7535\u8111\u8bbf\u95ee\u6a21\u578b\uff0c\u518d\u8003\u8651\u4fdd\u7559\uff1a
0.0.0.0
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u5341\u4e00\u3001\u666e\u901a\u7528\u6237\u6700\u5c11\u9700\u8981\u4fee\u6539\u54ea\u4e9b\u5730\u65b9\uff1f
\u5982\u679c\u4f60\u4e0d\u60f3\u7814\u7a76\u6240\u6709\u53c2\u6570\uff0c\u7b2c\u4e00\u6b21\u4f7f\u7528\u65f6\u81f3\u5c11\u786e\u8ba4\u4e0b\u9762 4 \u9879\u3002
\u7b2c\u4e00\u9879\uff1a
set “ROOT=C:\\Users\\Administrator\\llama.cpp”
\u4f5c\u7528\uff1a
\u586b\u5199\u4f60\u81ea\u5df1\u7684 llama.cpp \u5b89\u88c5\u76ee\u5f55\u3002
\u7b2c\u4e8c\u9879\uff1a
set “MODEL_DIR=D:\\ai\\Models\\llama\\OrcaRouter-Qwen3.8-Flash-Next-Uncensored-IQ4_XS”
\u4f5c\u7528\uff1a
\u586b\u5199\u4f60\u81ea\u5df1\u7684 GGUF \u6a21\u578b\u76ee\u5f55\u3002
\u7b2c\u4e09\u9879\uff1a
set “MODEL=%MODEL_DIR%\\Qwen3.8-Flash-Next-Uncensored-IQ4_XS-00001-of-00003.gguf”
\u4f5c\u7528\uff1a
\u586b\u5199\u4f60\u5b9e\u9645\u4e0b\u8f7d\u7684 GGUF \u7b2c\u4e00\u5206\u7247\u6587\u4ef6\u540d\u3002
\u7b2c\u56db\u9879\uff1a
set “GPU_DEVICE=CUDA0”
\u4f5c\u7528\uff1a
\u6307\u5b9a\u51c6\u5907\u4f7f\u7528\u7684 NVIDIA GPU\u3002
\u5355\u663e\u5361\u7528\u6237\u4e00\u822c\u4fdd\u6301 CUDA0\u3002
\u591a\u663e\u5361\u7528\u6237\u9700\u8981\u786e\u8ba4 5070 Ti \u5b9e\u9645\u5bf9\u5e94 CUDA0 \u8fd8\u662f CUDA1\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u5341\u4e8c\u3001RTX 5070 Ti 16GB \u53ef\u4ee5\u5148\u4f7f\u7528\u8fd9\u5957\u53c2\u6570
\u5982\u679c\u4f60\u7684\u786c\u4ef6\u4e5f\u662f RTX 5070 Ti 16GB\uff0c\u53ef\u4ee5\u5148\u4ece\u4e0b\u9762\u8fd9\u5957\u914d\u7f6e\u5f00\u59cb\uff1a
set “GPU_DEVICE=CUDA0”
set “FIT_TARGET=1536”
set “CONTEXT_SIZE=131072”
set “BATCH_SIZE=1024”
set “UBATCH_SIZE=256”
set “CPU_THREADS=8”
set “CPU_BATCH_THREADS=12”
\u5982\u679c\u6a21\u578b\u80fd\u591f\u7a33\u5b9a\u8fd0\u884c\uff0c\u5c31\u6ca1\u6709\u5fc5\u8981\u7ee7\u7eed\u4e3a\u4e86\u53c2\u6570\u800c\u53c2\u6570\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u5341\u4e09\u3001\u5982\u679c\u663e\u5b58\u4e0d\u8db3\uff0c\u5efa\u8bae\u6309\u7167\u8fd9\u4e2a\u987a\u5e8f\u4fee\u6539
\u7b2c\u4e00\u6b65\uff1a
\u628a FIT_TARGET \u4ece 1536 \u63d0\u9ad8\u5230 2048\u3002
set “FIT_TARGET=2048”
\u7b2c\u4e8c\u6b65\uff1a
\u5982\u679c\u8fd8\u662f\u663e\u5b58\u4e0d\u8db3\uff0c\u53ef\u4ee5\u5c1d\u8bd5\uff1a
set “FIT_TARGET=2560”
\u7b2c\u4e09\u6b65\uff1a
\u964d\u4f4e Batch\uff1a
set “BATCH_SIZE=512”
set “UBATCH_SIZE=128”
\u7b2c\u56db\u6b65\uff1a
\u5982\u679c\u5185\u5b58\u548c\u7f13\u5b58\u538b\u529b\u4ecd\u7136\u6bd4\u8f83\u5927\uff0c\u518d\u964d\u4f4e Context\uff1a
set “CONTEXT_SIZE=65536”
\u4e5f\u5c31\u662f 64K Context\u3002
\u5982\u679c\u4ecd\u7136\u6709\u538b\u529b\uff0c\u53ef\u4ee5\u7ee7\u7eed\u964d\u4f4e\u5230\uff1a
set “CONTEXT_SIZE=32768”
\u4e5f\u5c31\u662f 32K Context\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u5341\u56db\u3001\u7b2c\u4e00\u6b21\u542f\u52a8\u5e94\u8be5\u770b\u4ec0\u4e48\uff1f
\u7b2c\u4e00\u6b21\u8fd0\u884c\u811a\u672c\u65f6\uff0c\u5efa\u8bae\u5148\u4e0d\u8981\u542f\u52a8 Hermes\u3002
\u5148\u76f4\u63a5\u8fd0\u884c BAT \u811a\u672c\u3002
\u7136\u540e\u89c2\u5bdf llama.cpp \u7684\u542f\u52a8\u65e5\u5fd7\u3002
\u5982\u679c\u770b\u5230\u4e0b\u9762\u8fd9\u4e9b\u9519\u8bef\uff0c\u5c31\u8bf4\u660e\u914d\u7f6e\u8fd8\u6709\u95ee\u9898\uff1a
Model not found
\u8bf4\u660e\u6a21\u578b\u8def\u5f84\u6216\u8005\u6a21\u578b\u6587\u4ef6\u540d\u4e0d\u6b63\u786e\u3002
llama-server.exe not found
\u8bf4\u660e ROOT \u8def\u5f84\u4e0d\u6b63\u786e\u3002
CUDA out of memory
\u8bf4\u660e\u663e\u5b58\u4e0d\u8db3\uff0c\u9700\u8981\u63d0\u9ad8 FIT_TARGET\u3001\u964d\u4f4e Batch \u6216\u964d\u4f4e Context\u3002
unknown argument
\u901a\u5e38\u8bf4\u660e llama.cpp \u7248\u672c\u592a\u65e7\uff0c\u4e0d\u652f\u6301\u5f53\u524d\u53c2\u6570\u3002
failed to load model
\u8bf4\u660e\u6a21\u578b\u6587\u4ef6\u3001\u8def\u5f84\u3001\u5206\u7247\u6216\u8005 llama.cpp \u7248\u672c\u5b58\u5728\u95ee\u9898\u3002
\u5982\u679c\u6a21\u578b\u6b63\u5e38\u52a0\u8f7d\uff0c\u542f\u52a8\u65e5\u5fd7\u901a\u5e38\u8fd8\u80fd\u770b\u5230\u7c7b\u4f3c\uff1a
CUDA GPU
model buffer
KV buffer
compute buffer
offloaded layers
\u8fd9\u4e9b\u4fe1\u606f\u3002
\u6700\u540e llama-server \u5f00\u59cb\u76d1\u542c\u7aef\u53e3\u4ee5\u540e\uff0c\u8bf4\u660e\u6a21\u578b\u670d\u52a1\u5668\u5df2\u7ecf\u57fa\u672c\u542f\u52a8\u6210\u529f\u3002
\u7136\u540e\u518d\u6253\u5f00 Hermes\u3002
API \u5730\u5740\u586b\u5199\uff1a
http:\/\/127.0.0.1:8081\/v1
\u5373\u53ef\u5f00\u59cb\u6d4b\u8bd5\u3002
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500
\u4e8c\u5341\u4e94\u3001\u5173\u4e8e 17\uff5e18 tokens\/s \u7684\u8bf4\u660e
\u6211\u81ea\u5df1\u7684\u5b9e\u9645\u73af\u5883\u662f\uff1a
Windows
RTX 5070 Ti 16GB
llama.cpp b10718
Qwen3.8-Flash-Next IQ4_XS
128K Context
Q4_0 KV Cache
Flash Attention
Auto-Fit
Lazy Mode
Hermes Agent
\u5728\u8fd9\u5957\u73af\u5883\u4e0b\uff0c\u5b9e\u9645\u804a\u5929\u751f\u6210\u901f\u5ea6\u53ef\u4ee5\u8fbe\u5230\u5927\u7ea6\uff1a
17\uff5e18 tokens\/s
\u4f46\u8fd9\u4e2a\u6570\u5b57\u53ea\u662f\u6211\u81ea\u5df1\u7535\u8111\u7684\u5b9e\u6d4b\u7ed3\u679c\uff0c\u5e76\u4e0d\u4ee3\u8868\u6240\u6709 RTX 5070 Ti \u90fd\u4e00\u5b9a\u53ef\u4ee5\u5f97\u5230\u5b8c\u5168\u76f8\u540c\u7684\u901f\u5ea6\u3002
\u5b9e\u9645\u6027\u80fd\u8fd8\u4f1a\u53d7\u5230\u5f88\u591a\u56e0\u7d20\u5f71\u54cd\uff0c\u4f8b\u5982\uff1a
CPU \u6027\u80fd
\u7cfb\u7edf\u5185\u5b58\u5bb9\u91cf
\u5185\u5b58\u5e26\u5bbd
SSD \u6027\u80fd
GPU \u9a71\u52a8
CUDA
llama.cpp \u7248\u672c
GGUF \u91cf\u5316\u7248\u672c
Prompt \u957f\u5ea6
Context \u957f\u5ea6
\u540e\u53f0\u8fd0\u884c\u7684\u8f6f\u4ef6
GPU \u5f53\u524d\u663e\u5b58\u5360\u7528
\u6240\u4ee5\u5927\u5bb6\u53ef\u4ee5\u628a\u6211\u7684\u53c2\u6570\u5f53\u6210\u4e00\u4e2a\u8d77\u70b9\uff0c\u800c\u4e0d\u662f\u4e00\u4e2a\u7edd\u5bf9\u6807\u51c6\u3002<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
\u5b9e\u6d4b\u73af\u5883\uff1aWindows + RTX 5070 Ti 16GB + llama.cpp b10718 + Hermes Agent\u300217\uff5e18 tokens\/s \u4e3a\u672c\u673a\u5b9e\u6d4b\u503c\uff0c\u4e0d\u4ee3\u8868\u6240\u6709\u786c\u4ef6\u90fd\u80fd\u8fbe\u5230\u76f8\u540c\u901f\u5ea6\u3002<\/div>
\u00a0<\/div>
\u4e3a\u4ec0\u4e48 RTX 5070 Ti 16GB \u5355\u5361\u4e5f\u80fd\u8dd1 Qwen3.8-Flash-Next\uff0c\u5e76\u8fbe\u5230\u7ea6 18 tokens\/s\uff1f<\/h4>
\u8fd9\u5957\u542f\u52a8\u53c2\u6570\u662f\u6211\u9488\u5bf9\u00a0Windows + RTX 5070 Ti 16GB + llama.cpp b10718 + Hermes Agent<\/strong>\u00a0\u8c03\u6574\u7684\u4e00\u5957\u5355\u5361\u914d\u7f6e\u3002<\/p>
\u5b9e\u9645\u4f7f\u7528\u7684 GGUF \u662f IQ4_XS \u91cf\u5316\u7248\u672c\uff0c\u6a21\u578b\u6587\u4ef6\u672c\u8eab\u4f53\u79ef\u975e\u5e38\u5927\uff0c\u8fdc\u8fdc\u8d85\u8fc7 RTX 5070 Ti \u7684 16GB \u663e\u5b58\u3002<\/p>
\u4f46\u5b9e\u9645\u5728 Hermes \u4e2d\u5bf9\u8bdd\u65f6\uff0c\u6211\u8fd9\u5957\u914d\u7f6e\u53ef\u4ee5\u505a\u5230\u5927\u7ea6\uff1a<\/p>
17\uff5e18 tokens\/s<\/strong><\/p>
\u9700\u8981\u7279\u522b\u8bf4\u660e\uff1a\u8fd9\u91cc\u7684 18 t\/s \u662f\u6211\u672c\u673a\u5b9e\u9645\u8fd0\u884c\u5f97\u5230\u7684\u751f\u6210\u901f\u5ea6\uff0c\u5e76\u4e0d\u4ee3\u8868\u6240\u6709\u7535\u8111\u90fd\u4e00\u5b9a\u80fd\u8fbe\u5230\u76f8\u540c\u901f\u5ea6\u3002CPU\u3001\u5185\u5b58\u5e26\u5bbd\u3001SSD\u3001CUDA \u9a71\u52a8\u3001llama.cpp \u7248\u672c\u3001\u63d0\u793a\u8bcd\u957f\u5ea6\u4ee5\u53ca\u540e\u53f0\u7a0b\u5e8f\u90fd\u4f1a\u5f71\u54cd\u6700\u7ec8\u6027\u80fd\u3002<\/p>
1. 90GB \u5de6\u53f3\u7684\u6a21\u578b\uff0c\u4e3a\u4ec0\u4e48 16GB \u663e\u5361\u8fd8\u80fd\u8dd1\uff1f<\/h5>
\u8fd9\u4e5f\u662f\u8fd9\u4e2a\u6a21\u578b\u6700\u6709\u610f\u601d\u7684\u5730\u65b9\u3002<\/p>
Qwen3.8-Flash-Next \u5e76\u4e0d\u662f\u4f20\u7edf\u7684 100B+ Dense \u7a20\u5bc6\u6a21\u578b\u3002<\/p>
\u5b98\u65b9\u516c\u5e03\u7684\u8bed\u8a00\u6a21\u578b\u7ed3\u6784\u662f\uff1a<\/p>
  • \u603b\u8bed\u8a00\u6a21\u578b\u53c2\u6570\uff1a125B<\/li>
  • \u5b9e\u9645\u6bcf\u4e2a Token \u6fc0\u6d3b\u53c2\u6570\uff1a\u7ea6 6B<\/li>
  • N-gram Embedding\uff1a\u989d\u5916\u7ea6 51B<\/li>
  • MTP\uff1a\u7ea6 4B<\/li>
  • MoE Experts\uff1a512 \u4e2a<\/li>
  • \u6bcf\u6b21\u53ea\u6fc0\u6d3b 10 \u4e2a Routed Experts + 1 \u4e2a Shared Expert<\/li><\/ul>
    \u4e5f\u5c31\u662f\u8bf4\uff1a<\/p>
    \u6a21\u578b\u867d\u7136\u975e\u5e38\u5927\uff0c\u4f46\u751f\u6210\u6bcf\u4e2a Token \u65f6\uff0c\u5e76\u4e0d\u4f1a\u628a\u5168\u90e8 125B \u53c2\u6570\u90fd\u53c2\u4e0e\u8ba1\u7b97\u3002<\/strong><\/p>
    \u771f\u6b63\u53c2\u4e0e\u4e00\u6b21\u524d\u5411\u63a8\u7406\u7684\u53c2\u6570\u89c4\u6a21\u53ea\u6709\u5927\u7ea6 6B\u3002<\/p>
    \u8fd9\u5c31\u662f\u4e3a\u4ec0\u4e48\u5b83\u548c\u4f20\u7edf\u7684 100B\u3001120B Dense \u6a21\u578b\u5b8c\u5168\u4e0d\u662f\u4e00\u4e2a\u6982\u5ff5\u3002<\/p>
    \u53ef\u4ee5\u7b80\u5355\u7406\u89e3\u4e3a\uff1a<\/p>
    \u786c\u76d8\u548c\u5185\u5b58\u8d1f\u8d23\u201c\u5b58\u4e0b\u6574\u4e2a\u77e5\u8bc6\u5e93\u201d\uff0cGPU \u53ea\u91cd\u70b9\u8ba1\u7b97\u5f53\u524d Token \u771f\u6b63\u9700\u8981\u7528\u5230\u7684\u90a3\u90e8\u5206\u4e13\u5bb6\u3002<\/blockquote>
    \u6240\u4ee5\u8fd9\u79cd\u6a21\u578b\u7279\u522b\u9002\u5408\uff1a<\/p>
    \u5927\u5185\u5b58 + \u4e2d\u9ad8\u7aef\u6d88\u8d39\u7ea7\u663e\u5361<\/strong><\/p>
    \u8fd9\u79cd\u672c\u5730\u90e8\u7f72\u65b9\u5f0f\u3002<\/p>
    2. 51B N-gram \u53c2\u6570\u4e3a\u4ec0\u4e48\u6ca1\u6709\u628a\u901f\u5ea6\u5f7b\u5e95\u62d6\u6b7b\uff1f<\/h5>
    Qwen3.8-Flash-Next \u8fd8\u6709\u4e00\u4e2a\u975e\u5e38\u7279\u6b8a\u7684\u8bbe\u8ba1\uff1a<\/p>
    51B N-gram Embedding\u3002<\/strong><\/p>
    \u8fd9\u4e00\u90e8\u5206\u53c2\u6570\u5360\u636e\u4e86\u975e\u5e38\u5927\u7684\u6a21\u578b\u6587\u4ef6\u4f53\u79ef\uff0c\u4f46\u5b83\u548c\u666e\u901a Transformer \u6743\u91cd\u4e0d\u4e00\u6837\u3002<\/p>
    Qwen \u5b98\u65b9\u660e\u786e\u63d0\u5230\uff0cN-gram Embedding \u7684\u7279\u70b9\u5c31\u662f\uff1a<\/p>
    \u53c2\u6570\u53ef\u4ee5\u505a\u5f97\u5f88\u5927\uff0c\u4f46\u8ba1\u7b97\u91cf\u6bd4\u8f83\u4f4e\uff0c\u540c\u65f6\u66f4\u52a0\u9002\u5408\u8fdb\u884c Offload\u3002<\/strong><\/p>
    \u6240\u4ee5\u770b\u5230\u6a21\u578b\u51e0\u5341 GB\u3001\u751a\u81f3\u63a5\u8fd1 100GB \u65f6\uff0c\u4e0d\u8981\u76f4\u63a5\u6309\u7167\u4f20\u7edf Dense \u6a21\u578b\u7684\u601d\u8def\u5224\u65ad\u6027\u80fd\u3002<\/p>
    \u5b83\u5b9e\u9645\u4e0a\u5c5e\u4e8e\u4e00\u79cd\uff1a<\/p>
    \u201c\u5b58\u50a8\u5f88\u5927\uff0c\u4f46\u6bcf Token \u5b9e\u9645\u8ba1\u7b97\u91cf\u76f8\u5bf9\u5c0f\u201d<\/strong><\/p>
    \u7684\u6a21\u578b\u67b6\u6784\u3002<\/p>
    \u8fd9\u4e5f\u662f Qwen3.8-Flash-Next \u80fd\u5728\u6d88\u8d39\u7ea7\u786c\u4ef6\u4e0a\u51fa\u73b0\u975e\u5e38\u6709\u610f\u601d\u6027\u80fd\u8868\u73b0\u7684\u91cd\u8981\u539f\u56e0\u3002<\/p>
    3. `–lazy-mode on` \u662f\u8fd9\u5957\u914d\u7f6e\u5f88\u91cd\u8981\u7684\u4e00\u9879<\/h5>
    \u542f\u52a8\u53c2\u6570\u91cc\u6709\uff1a<\/p>
    \n--load-mode mmap\n--lazy-mode on\n<\/code><\/pre>
    \u8fd9\u662f\u6211\u4fdd\u7559\u7684\u91cd\u8981\u53c2\u6570\u3002<\/p>
    \u65b0\u7248 llama.cpp \u4e13\u95e8\u9488\u5bf9\u8fd9\u79cd\u62e5\u6709\u5de8\u5927 N-gram \/ \u7279\u6b8a Embedding Tensor \u7684\u6a21\u578b\u589e\u52a0\u4e86 Lazy Tensor Read \u673a\u5236\u3002<\/p>
    \u540e\u6765\u8be5\u53c2\u6570\u88ab\u6b63\u5f0f\u6539\u540d\u4e3a\uff1a<\/p>
    \n--lazy-mode\n<\/code><\/pre>
    \u5b83\u7684\u6838\u5fc3\u601d\u60f3\u5c31\u662f\uff1a<\/p>
    \u67d0\u4e9b\u8d85\u5927\u7684 Tensor \u4e0d\u9700\u8981\u542f\u52a8\u65f6\u5168\u90e8\u5e38\u9a7b\u5185\u5b58\uff0c\u800c\u662f\u5728\u771f\u6b63\u9700\u8981\u65f6\u8bfb\u53d6\u5bf9\u5e94\u7684\u6570\u636e\u3002<\/strong><\/p>
    \u8fd9\u5bf9 Qwen3.8-Flash-Next \u8fd9\u79cd\u62e5\u6709\u8d85\u5927 N-gram Embedding \u7684\u6a21\u578b\u7279\u522b\u91cd\u8981\u3002<\/p>
    \u6240\u4ee5\uff1a<\/p>
    \n--load-mode mmap\n--lazy-mode on\n<\/code><\/pre>
    \u5e76\u4e0d\u662f\u5355\u7eaf\u4e3a\u4e86\u201c\u52a0\u8f7d\u5feb\u4e00\u70b9\u201d\u3002<\/p>
    \u771f\u6b63\u7684\u76ee\u7684\uff0c\u662f\u8ba9\u8fd9\u79cd\u8fdc\u8fdc\u5927\u4e8e\u663e\u5b58\u5bb9\u91cf\u7684\u6a21\u578b\u80fd\u591f\u66f4\u5408\u7406\u5730\u5229\u7528\uff1a<\/p>
    SSD \u2192 \u7cfb\u7edf\u5185\u5b58 \u2192 GPU<\/strong><\/p>
    \u8fd9\u4e00\u6574\u5957\u5b58\u50a8\u5c42\u7ea7\u3002<\/p>
    4. \u4e3a\u4ec0\u4e48\u4f7f\u7528 `–fit on`<\/h5>
    \u914d\u7f6e\u91cc\u8fd8\u6709\uff1a<\/p>
    \n--fit on\n--fit-target 1536\n<\/code><\/pre>
    \u8fd9\u662f\u53e6\u5916\u4e00\u4e2a\u975e\u5e38\u5173\u952e\u7684\u5730\u65b9\u3002<\/p>
    \u6211\u6ca1\u6709\u624b\u5de5\u5199\uff1a<\/p>
    \n-ngl 20\n-ngl 30\n-ngl 40\n<\/code><\/pre>
    \u6765\u786c\u6027\u89c4\u5b9a\u5230\u5e95\u591a\u5c11\u5c42\u653e\u8fdb\u663e\u5361\u3002<\/p>
    \u800c\u662f\u4ea4\u7ed9\u65b0\u7248 llama.cpp \u7684 Auto-Fit \u7cfb\u7edf\u81ea\u52a8\u8ba1\u7b97\u3002<\/p>
    --fit on<\/code>\u00a0\u4f1a\u6839\u636e\u5f53\u524d GPU \u53ef\u7528\u663e\u5b58\u81ea\u52a8\u8c03\u6574\u6ca1\u6709\u88ab\u624b\u5de5\u56fa\u5b9a\u7684\u5185\u5b58\u5206\u914d\u53c2\u6570\u3002<\/p>
    --fit-target<\/code>\u00a0\u53ef\u4ee5\u7406\u89e3\u4e3a\uff1a<\/p>
    \u5e0c\u671b GPU \u6700\u7ec8\u4fdd\u7559\u591a\u5c11 MiB \u7684\u7a7a\u95f2\u663e\u5b58\u3002<\/strong><\/p>
    \u6211\u7684\u8bbe\u7f6e\u662f\uff1a<\/p>
    \n--fit-target 1536\n<\/code><\/pre>
    \u4e5f\u5c31\u662f\uff1a<\/p>
    \u5c3d\u91cf\u4f7f\u7528 RTX 5070 Ti \u7684\u663e\u5b58\uff0c\u4f46\u76ee\u6807\u7559\u4e0b\u5927\u7ea6 1.5GB \u5b89\u5168\u7a7a\u95f4\u3002<\/strong><\/p>
    \u8fd9\u4e48\u505a\u4e3b\u8981\u662f\u4e3a\u4e86\u907f\u514d\u4e00\u79cd\u5e38\u89c1\u60c5\u51b5\uff1a<\/p>
    \u6a21\u578b\u521a\u52a0\u8f7d\u8fdb\u53bb\u6ca1\u95ee\u9898\uff0c\u4f46 Hermes \u4e00\u5f00\u59cb\u957f\u5bf9\u8bdd\u3001\u5efa\u7acb KV Cache \u6216 CUDA \u521b\u5efa\u989d\u5916\u8ba1\u7b97 Buffer \u540e\u7a81\u7136\uff1a<\/p>
    \nCUDA out of memory\n<\/code><\/pre>
    \u56e0\u6b64\u4e0d\u662f\u628a 16GB \u663e\u5b58\u5403\u5230 16GB \u624d\u6700\u5feb\u3002<\/p>
    \u5bf9\u4e8e\u65e5\u5e38 Hermes Agent \u4f7f\u7528\uff0c\u6211\u66f4\u503e\u5411\u4e8e\uff1a<\/p>
    \u7a0d\u5fae\u727a\u7272\u4e00\u70b9\u7406\u8bba\u6700\u5927 GPU Offload\uff0c\u6362\u53d6\u957f\u671f\u7a33\u5b9a\u8fd0\u884c\u3002<\/strong><\/p>
    5. \u4e3a\u4ec0\u4e48\u53ea\u4f7f\u7528 RTX 5070 Ti\uff0c\u4e0d\u4f7f\u7528 RTX 3070\uff1f<\/h5>
    \u6211\u7684\u7535\u8111\u91cc\u8fd8\u6709\u4e00\u5f20 RTX 3070\u3002<\/p>
    \u4f46\u662f\u8fd9\u4e2a\u914d\u7f6e\u660e\u786e\u5199\u4e86\uff1a<\/p>
    \n--device CUDA0\n--split-mode none\n<\/code><\/pre>
    \u76ee\u7684\u5c31\u662f\uff1a<\/p>
    \u53ea\u4f7f\u7528 RTX 5070 Ti\u3002<\/strong><\/p>
    \u4e0d\u8ba9 RTX 3070 \u52a0\u5165\u6a21\u578b\u5206\u5272\u3002<\/p>
    \u5f88\u591a\u4eba\u7b2c\u4e00\u53cd\u5e94\u53ef\u80fd\u4f1a\u89c9\u5f97\uff1a<\/p>
    \u4e24\u5f20\u663e\u5361\u52a0\u8d77\u6765\u4e0d\u662f\u663e\u5b58\u66f4\u591a\u5417\uff1f<\/blockquote>
    \u6ca1\u9519\u3002<\/p>
    \u4f46\uff1a<\/p>
    \u663e\u5b58\u589e\u52a0 \u2260 Token \u751f\u6210\u901f\u5ea6\u4e00\u5b9a\u589e\u52a0\u3002<\/strong><\/p>
    \u53cc GPU \u63a8\u7406\u4f1a\u589e\u52a0\uff1a<\/p>