#Troubleshooting float16 is only supported on GPUs with compute capability at least xx Error in vLLM

#Problem Description

#Environment

• Hardware: NVIDIA GPUs with compute capability <8.0 (e.g., Tesla V100, T4)
• Model Types: LLMs requiring bfloat16/FP8 precision (e.g., LLaMA-2-70B, GPT-NeoX-20B)

#Symptoms

1. Explicit error message:

   ValueError: float16/bfloat16 is only supported on GPUs with compute capability at least 8.0
2. Failed kernel compilation during model loading

#Related Logs

# vLLM error stack trace
File "/usr/local/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/__init__.py", line 37, in _verify_cuda_compute_capability
    raise ValueError(
ValueError: bfloat16 is only supported on GPUs with compute capability at least 8.0. Current GPU: Tesla V100-PCIE-16GB, compute capability 7.0

#Root Cause

#Primary Cause

Insufficient GPU Compute Capability The GPU's compute capability (CC) doesn't meet the minimum requirement for specific data types:

• bfloat16/FP8: Requires CC ≥8.0 (Ampere architecture or newer)
• FP16 Tensor Core Optimization: Requires CC \≥7.0 (Volta architecture or newer)

#Technical Analysis

1. Architecture Limitations:

   • Pre-Ampere GPUs (CC <8.0) lack dedicated matrix math units for bfloat16 operations
   • Tensor Cores in Volta/Turing (CC 7.0-7.5) only support FP16/FP32 mixed precision

2. Framework Enforcement:

   # vLLM's capability check (simplified)
   def _verify_cuda_compute_capability():
       if device.compute_capability < MIN_REQUIRED_CC:
           raise ValueError(f"Requires compute capability ≥{MIN_REQUIRED_CC}")

#Troubleshooting

#Step 1: Verify GPU Compute Capability

import torch
print(f"Compute Capability: {torch.cuda.get_device_capability()}")

#Step 2: Check Model Precision Requirements

cat model/config.json | grep "torch_dtype"
# Expected output: "bfloat16" or "float16"

#Step 3: Validate Framework Compatibility

from vllm import _is_cuda_compute_capability_compatible as compat
print(f"bfloat16 supported: {compat((8,0))}")

#Solution

#Solution for Insufficient Compute Capability

#Considerations

• Performance degradation expected when downgrading precision
• Model accuracy may vary with different precision types

#Prerequisites

• CUDA Toolkit ≥11.8

#Steps

1. Modify InferenceService yaml: add args like --dtype=half

   apiVersion: serving.kserve.io/v1beta1
   kind: InferenceService
   metadata:
     name: llama-2-service
     annotations:
       serving.kserve.io/enable-prometheus-scraping: 'true'
   spec:
     predictor:
       containers:
         - name: kserve-container
           image: vllm/vllm-serving:0.3.2
           args:
             - --model=meta-llama/Llama-2-7b-chat-hf
             - --dtype=half # Force FP16 precision
             - --tensor-parallel-size=1
           resources:
             limits:
               nvidia.com/gpu: '1'
2. Wait deploy restart

#Preventive Measures

1. Pre-Flight Checks:

   from vllm import LLM
   LLM.validate_environment(model_dtype="bfloat16")

2. Cluster Configuration:

   # NVIDIA device plugin config
   helm upgrade -i nvidia-device-plugin \
     --set compatabilityPolicy=strict \
     --set computeCapabilities=8.0+

3. Model Optimization:

   # Apply AWQ quantization
   llm = LLM(model="codellama/CodeLlama-34b",
             quantization="awq",
             load_format="awq")

#Related Content

#GPU Compute Capability Reference

#Official References

1. NVIDIA Compute Capability Table
2. vLLM Hardware Requirements