vllm - 💡(How to fix) Fix [Usage]: Unable to run Qwen3-14B with vLLM (multiple issues) [2 comments, 3 participants]

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vllm-project/vllm#37907Fetched 2026-04-08 01:22:46
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Attempting to serve Qwen/Qwen3-14B with vLLM fails in sequence across three distinct issues. Each is documented below with the error, root cause, and workaround (where found). Issue #3 remains unresolved.


Error Message

ImportError: libcudart.so.12: cannot open shared object file: No such file or directory

Root Cause

Attempting to serve Qwen/Qwen3-14B with vLLM fails in sequence across three distinct issues. Each is documented below with the error, root cause, and workaround (where found). Issue #3 remains unresolved.

Fix Action

Fix / Workaround

============================== CPU Info

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: AuthenticAMD Model name: AMD Ryzen 7 1700X Eight-Core Processor CPU family: 23 Model: 1 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 1 Frequency boost: enabled CPU(s) scaling MHz: 100% CPU max MHz: 3800.0000 CPU min MHz: 2200.0000 BogoMIPS: ~6800.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl overflow_recov succor smca Virtualization: AMD-V L1d cache: 256 KiB (8 instances) L1i cache: 512 KiB (8 instances) L2 cache: 4 MiB (8 instances) L3 cache: 16 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP disabled Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled Vulnerability Meltdown: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Tsx async abort: Not affected

Attempting to serve Qwen/Qwen3-14B with vLLM fails in sequence across three distinct issues. Each is documented below with the error, root cause, and workaround (where found). Issue #3 remains unresolved.

Workaround (found via [#31018](https://github.com/vllm-project/vllm/issues/31018#issuecomment-3784610216)):

export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
export CUDA_VERSION=130
export CPU_ARCH=$(uname -m)

Code Example

(root) root@vllm01:~# python collect_env.py
Collecting environment information...
uv is set
==============================
        System Info
==============================
OS                           : Debian GNU/Linux forky/sid (x86_64)
GCC version                  : (Debian 15.2.0-15) 15.2.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.42

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar 10 2026, 18:17:25) [Clang 21.1.4 ] (64-bit runtime)
Python platform              : Linux-6.17.0-19-generic-x86_64-with-glibc2.42

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA GeForce RTX 5060 Ti
GPU 1: NVIDIA GeForce RTX 5060 Ti

Nvidia driver version        : 580.126.20
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  16
On-line CPU(s) list:                     0-15
Vendor ID:                               AuthenticAMD
Model name:                              AMD Ryzen 7 1700X Eight-Core Processor
CPU family:                              23
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      8
Socket(s):                               1
Stepping:                                1
Frequency boost:                         enabled
CPU(s) scaling MHz:                      100%
CPU max MHz:                             3800.0000
CPU min MHz:                             2200.0000
BogoMIPS:                                ~6800.00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl overflow_recov succor smca
Virtualization:                          AMD-V
L1d cache:                               256 KiB (8 instances)
L1i cache:                               512 KiB (8 instances)
L2 cache:                                4 MiB (8 instances)
L3 cache:                                16 MiB (2 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-15
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB conditional; STIBP disabled
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled
Vulnerability Meltdown:                  Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Tsx async abort:           Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.15.1.9
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu130
[pip3] torch-c-dlpack-ext==0.1.5
[pip3] torchaudio==2.10.0+cu130
[pip3] torchvision==0.25.0+cu130
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	GPU0	GPU1	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	PHB	0-31	0		N/A
GPU1	PHB	 X 	0-31	0		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

==============================
     Environment Variables
==============================
CUDA_VERSION=130
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

---

uv run --with vllm vllm --help

---

ImportError: libcudart.so.12: cannot open shared object file: No such file or directory

---

export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
export CUDA_VERSION=130
export CPU_ARCH=$(uname -m)

uv pip install \
  https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu${CUDA_VERSION}-cp38-abi3-manylinux_2_35_${CPU_ARCH}.whl \
  --extra-index-url https://download.pytorch.org/whl/cu${CUDA_VERSION} \
  --index-strategy unsafe-best-match

---

uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching

---

RuntimeError: Worker failed with error 'RuntimeError: Failed to find C compiler.
Please specify via CC environment variable or set triton.knobs.build.impl.'

---

apt-get install build-essential

---

uv run vllm serve Qwen/Qwen3-14B -tp 2
uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --enforce-eager
uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --enforce-eager --gpu-memory-utilization 0.85
PYTORCH_ALLOC_CONF=expandable_segments:True uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --enforce-eager --gpu-memory-utilization 0.80 --max-num-seqs 32 --max-model-len 8192
PYTORCH_ALLOC_CONF=expandable_segments:True uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --gpu-memory-utilization 0.89

---

RuntimeError: Worker failed with error 'RuntimeError: Failed to run autotuning code block:
CUDA out of memory. Tried to allocate 742.00 MiB.
GPU 0 has a total capacity of 15.46 GiB of which 304.50 MiB is free.
Including non-PyTorch memory, this process has 14.35 GiB memory in use.
Of the allocated memory 13.90 GiB is allocated by PyTorch, and 18.53 MiB is reserved
by PyTorch but unallocated.'
RAW_BUFFERClick to expand / collapse

Your current environment

(root) root@vllm01:~# python collect_env.py
Collecting environment information...
uv is set
==============================
        System Info
==============================
OS                           : Debian GNU/Linux forky/sid (x86_64)
GCC version                  : (Debian 15.2.0-15) 15.2.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.42

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar 10 2026, 18:17:25) [Clang 21.1.4 ] (64-bit runtime)
Python platform              : Linux-6.17.0-19-generic-x86_64-with-glibc2.42

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : Could not collect
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : 
GPU 0: NVIDIA GeForce RTX 5060 Ti
GPU 1: NVIDIA GeForce RTX 5060 Ti

Nvidia driver version        : 580.126.20
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  16
On-line CPU(s) list:                     0-15
Vendor ID:                               AuthenticAMD
Model name:                              AMD Ryzen 7 1700X Eight-Core Processor
CPU family:                              23
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      8
Socket(s):                               1
Stepping:                                1
Frequency boost:                         enabled
CPU(s) scaling MHz:                      100%
CPU max MHz:                             3800.0000
CPU min MHz:                             2200.0000
BogoMIPS:                                ~6800.00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl overflow_recov succor smca
Virtualization:                          AMD-V
L1d cache:                               256 KiB (8 instances)
L1i cache:                               512 KiB (8 instances)
L2 cache:                                4 MiB (8 instances)
L3 cache:                                16 MiB (2 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-15
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB conditional; STIBP disabled
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled
Vulnerability Meltdown:                  Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Tsx async abort:           Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.15.1.9
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu130
[pip3] torch-c-dlpack-ext==0.1.5
[pip3] torchaudio==2.10.0+cu130
[pip3] torchvision==0.25.0+cu130
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	GPU0	GPU1	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	PHB	0-31	0		N/A
GPU1	PHB	 X 	0-31	0		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

==============================
     Environment Variables
==============================
CUDA_VERSION=130
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

How would you like to use vllm

I'm Unable to run Qwen3-14B with vLLM on dual-GPU setup as I encoutered multiple issues.

Environment

ModelQwen/Qwen3-14B
GPUs2× (15.46 GiB VRAM each)
Tensor Parallelism-tp 2
vLLM installvia uv

Summary

Attempting to serve Qwen/Qwen3-14B with vLLM fails in sequence across three distinct issues. Each is documented below with the error, root cause, and workaround (where found). Issue #3 remains unresolved.


Issue 1 — Missing CUDA shared library after following official install docs

Command:

uv run --with vllm vllm --help

Error:

ImportError: libcudart.so.12: cannot open shared object file: No such file or directory

Workaround (found via [#31018](https://github.com/vllm-project/vllm/issues/31018#issuecomment-3784610216)):

export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
export CUDA_VERSION=130
export CPU_ARCH=$(uname -m)

uv pip install \
  https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu${CUDA_VERSION}-cp38-abi3-manylinux_2_35_${CPU_ARCH}.whl \
  --extra-index-url https://download.pytorch.org/whl/cu${CUDA_VERSION} \
  --index-strategy unsafe-best-match

Request: This workaround should be documented in the [quickstart guide](https://docs.vllm.ai/en/latest/getting_started/quickstart/#installation). The default uv-based install produces a non-working setup on this configuration with no clear guidance.


Issue 2 — Missing build-essential / C compiler not documented as a prerequisite

Command:

uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching

Error:

RuntimeError: Worker failed with error 'RuntimeError: Failed to find C compiler.
Please specify via CC environment variable or set triton.knobs.build.impl.'

Fix:

apt-get install build-essential

Request: build-essential (or equivalent) should be listed as a system prerequisite in the installation docs.


Issue 3 — CUDA OOM during autotuning despite -tp 2 and reduced settings (unresolved)

After resolving issues 1 and 2, all serve commands fail with an OOM error during Triton autotuning.

Commands tried:

uv run vllm serve Qwen/Qwen3-14B -tp 2
uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --enforce-eager
uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --enforce-eager --gpu-memory-utilization 0.85
PYTORCH_ALLOC_CONF=expandable_segments:True uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --enforce-eager --gpu-memory-utilization 0.80 --max-num-seqs 32 --max-model-len 8192
PYTORCH_ALLOC_CONF=expandable_segments:True uv run vllm serve Qwen/Qwen3-14B -tp 2 --enable-prefix-caching --gpu-memory-utilization 0.89

Error:

RuntimeError: Worker failed with error 'RuntimeError: Failed to run autotuning code block:
CUDA out of memory. Tried to allocate 742.00 MiB.
GPU 0 has a total capacity of 15.46 GiB of which 304.50 MiB is free.
Including non-PyTorch memory, this process has 14.35 GiB memory in use.
Of the allocated memory 13.90 GiB is allocated by PyTorch, and 18.53 MiB is reserved
by PyTorch but unallocated.'

Notes:

  • No other processes are using significant GPU memory (~800Mb)
  • Tried PYTORCH_ALLOC_CONF=expandable_segments:True as suggested in the error message — no effect
  • Also tried Qwen/Qwen2.5-14B-Instruct as a baseline — same failure
  • The OOM seem to occur during autotuning, before any inference

Question: Is Qwen3-14B expected to fit on 2× 15.46 GiB GPUs with -tp 2? Is there a way to disable or cap memory usage during the autotuning phase?

Before submitting a new issue...

  • Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

extent analysis

Fix Plan

To address Issue 3, the CUDA OOM error during autotuning, several steps can be taken:

  1. Reduce Model Parallelism: Try reducing the tensor parallelism (-tp) to 1 to see if the model can fit on a single GPU.
  2. Adjust Autotuning Settings: Disable autotuning or adjust its settings to reduce memory usage. This can be done by adding the --autotune-min-threads and --autotune-max-threads flags to the vllm serve command.
  3. Increase GPU Memory Utilization: Increase the --gpu-memory-utilization flag to a higher value (e.g., 0.95) to allow the model to use more GPU memory.
  4. Reduce Batch Size: Reduce the batch size to reduce memory usage during inference.
  5. Use Model Pruning or Quantization: Consider using model pruning or quantization techniques to reduce the model's memory footprint.

Example command:

uv run vllm serve Qwen/Qwen3-14B -tp 1 --enable-prefix-caching --autotune-min-threads 1 --autotune-max-threads 4 --gpu-memory-utilization 0.95

Verification

To verify that the fix worked, check the GPU memory usage during autotuning and inference using tools like nvidia-smi or gpu-memory-info. If the model is able to run without OOM errors, it indicates that the fix was successful.

Extra Tips

  • Monitor GPU memory usage and adjust the settings accordingly.
  • Consider using a larger GPU or distributing the model across multiple GPUs.
  • Experiment with different autotuning settings and batch sizes to find the optimal configuration for your model and hardware.

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