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ConsumerGeForce RTX 20

NVIDIA RTX 2080 CUDA Performance Guide: Specs, Benchmarks & Optimization

December 25, 20259 min read

Introduction

The NVIDIA GeForce RTX 2080 was the first generation consumer GPU with Tensor Cores, introducing hardware-accelerated mixed precision training. With 2,944 CUDA cores and 8GB GDDR6, it provided a significant leap for ML developers when released. For CUDA developers with an RTX 2080, it still provides useful Tensor Core capabilities for smaller workloads. The 8GB VRAM is the main limitation in 2025, but the 1st gen Tensor Cores still accelerate FP16 operations meaningfully. This guide covers the RTX 2080's specifications, realistic capabilities, and tips for maximizing performance on this aging but still capable GPU.

Specifications

Architecture	Turing (TU104)
CUDA Cores	2,944
Tensor Cores	368
Memory	8GB GDDR6
Memory Bandwidth	448 GB/s
Base / Boost Clock	1515 / 1800 MHz
FP32 Performance	10.6 TFLOPS
FP16 Performance	21.2 TFLOPS
L2 Cache	4MB
TDP	225W
NVLink	Yes
MSRP	$799
Release	September 2018

Key Features

2,944 CUDA cores
8GB GDDR6 memory
1st Gen Tensor Cores
NVLink support
FP16 acceleration
PCIe 3.0 x16 interface
CUDA Compute Capability 7.5
First consumer Tensor Core GPU
Still capable for small workloads
225W TDP

CUDA Optimization Tips

1.Use FP16 mixed precision training
2.Keep models under 6GB
3.Leverage Tensor Cores for matrix operations
4.Use NVLink if you have two cards
5.Profile memory usage carefully
6.Consider gradient checkpointing
7.Optimize batch sizes for 8GB
8.Use modern CUDA for best Turing support
9.Profile with Nsight Compute
10.Consider upgrading for larger models

Code Examples

RTX 2080 Setup and Memory Check

This code snippet shows how to detect your RTX 2080, check available memory, and configure optimal settings for the Turing (TU104) architecture.

python

import torch
import pynvml

# Check if RTX 2080 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")

# RTX 2080 Memory: 8GB - Optimal batch sizes
# Architecture: Turing (TU104)
# CUDA Cores: 2,944

# Memory-efficient training for RTX 2080
torch.backends.cuda.matmul.allow_tf32 = True  # Enable TF32 for Turing (TU104)
torch.backends.cudnn.allow_tf32 = True

# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 8 GB total")

# Recommended batch size calculation for RTX 2080
model_memory_gb = 2.0  # Adjust based on your model
batch_multiplier = (8 - model_memory_gb) / 4  # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 2080: {recommended_batch}")

Benchmarks

Task	Performance	Comparison
ResNet-50 Training (imgs/sec)	350	FP16 mixed precision
BERT-Base Inference (sentences/sec)	380	FP16 mode
Stable Diffusion (512x512, sec/img)	15	Slow, memory limited
Small Model Training	Adequate	Still useful
cuBLAS SGEMM 4096x4096 (TFLOPS)	10	94% efficiency
Memory Bandwidth (GB/s measured)	420	94% efficiency

Use Cases

Use Case	Rating	Notes
Small Model Training	Fair	8GB is limiting in 2025
ML Inference	Fair	Works but dated
Learning CUDA	Good	Tensor Cores for learning
Scientific Computing	Fair	Adequate FP32
Production ML	Poor	Too limited now
Large Models	Poor	8GB insufficient

Pros and Cons

Pros

+Has Tensor Cores
+NVLink support
+Reasonable used prices
+Still supported by frameworks
+Good for small workloads
+Decent bandwidth

Cons

−8GB very limiting
−First-gen Tensor Cores
−No TF32 or FP8
−Aging architecture
−Higher power than successors
−RTX 3060 12GB better value

Frequently Asked Questions

Is RTX 2080 still good for ML in 2025?

For small experiments and learning, it is usable. The 8GB VRAM is the main limitation. For serious ML work, RTX 3060 12GB or newer is strongly recommended.

How does RTX 2080 compare to RTX 3060?

RTX 3060 is slightly slower in raw compute but has 12GB vs 8GB VRAM and newer Tensor Cores. For ML, the 3060 is significantly more useful due to the extra memory.

Should I upgrade from RTX 2080?

If you do ML work that needs more than 8GB, yes. RTX 3060 12GB, RTX 4060, or RTX 4070 Super are good upgrade paths depending on budget and needs.

Can RTX 2080 NVLink help with ML?

Yes, two RTX 2080s with NVLink can share 16GB memory and increase compute. However, buying a single newer card like RTX 4070 Super (12GB) or 4070 Ti Super (16GB) is usually better.

Alternatives

12GB, better for ML

Much faster, 8GB

Ada, FP8, same 8GB

11GB, 35% faster

Ready to optimize your CUDA kernels for RTX 2080? Download RightNow AI for real-time performance analysis.

RTX 2080 CUDARTX 2080 specsRTX 2080 machine learningRTX 2080 deep learningRTX 2080 tensor coresRTX 2080 vs 3070

Introduction

CUDA Optimization Tips

1.Use FP16 mixed precision training

2.Keep models under 6GB

3.Leverage Tensor Cores for matrix operations

4.Use NVLink if you have two cards

5.Profile memory usage carefully

6.Consider gradient checkpointing

7.Optimize batch sizes for 8GB

8.Use modern CUDA for best Turing support

9.Profile with Nsight Compute

10.Consider upgrading for larger models

Code Examples

RTX 2080 Setup and Memory Check

This code snippet shows how to detect your RTX 2080, check available memory, and configure optimal settings for the Turing (TU104) architecture.

python

import torch
import pynvml

# Check if RTX 2080 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")

# RTX 2080 Memory: 8GB - Optimal batch sizes
# Architecture: Turing (TU104)
# CUDA Cores: 2,944

# Memory-efficient training for RTX 2080
torch.backends.cuda.matmul.allow_tf32 = True  # Enable TF32 for Turing (TU104)
torch.backends.cudnn.allow_tf32 = True

# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 8 GB total")

# Recommended batch size calculation for RTX 2080
model_memory_gb = 2.0  # Adjust based on your model
batch_multiplier = (8 - model_memory_gb) / 4  # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for RTX 2080: {recommended_batch}")

Benchmarks

Task	Performance	Comparison
ResNet-50 Training (imgs/sec)	350	FP16 mixed precision
BERT-Base Inference (sentences/sec)	380	FP16 mode
Stable Diffusion (512x512, sec/img)	15	Slow, memory limited
Small Model Training	Adequate	Still useful
cuBLAS SGEMM 4096x4096 (TFLOPS)	10	94% efficiency
Memory Bandwidth (GB/s measured)	420	94% efficiency

Use Case

Rating

Notes

Small Model Training

Fair

8GB is limiting in 2025

ML Inference

Fair

Works but dated

Learning CUDA

Good

Tensor Cores for learning

Scientific Computing

Fair

Adequate FP32

Production ML

Poor

Too limited now

Large Models

Poor

8GB insufficient

Frequently Asked Questions

Is RTX 2080 still good for ML in 2025?

For small experiments and learning, it is usable. The 8GB VRAM is the main limitation. For serious ML work, RTX 3060 12GB or newer is strongly recommended.

How does RTX 2080 compare to RTX 3060?

RTX 3060 is slightly slower in raw compute but has 12GB vs 8GB VRAM and newer Tensor Cores. For ML, the 3060 is significantly more useful due to the extra memory.

Should I upgrade from RTX 2080?

If you do ML work that needs more than 8GB, yes. RTX 3060 12GB, RTX 4060, or RTX 4070 Super are good upgrade paths depending on budget and needs.

Can RTX 2080 NVLink help with ML?

Yes, two RTX 2080s with NVLink can share 16GB memory and increase compute. However, buying a single newer card like RTX 4070 Super (12GB) or 4070 Ti Super (16GB) is usually better.