Head-to-Head ComparisonUpdated May 27, 2026AI Inference & Mid-Tier LLMs

NVIDIA RTX 5080 vs NVIDIA RTX 4090

for AI Inference & Mid-Tier LLMs

TL;DR

The RTX 5080 offers competitive raw FP16 throughput and improved memory bandwidth efficiency at a lower price, but the RTX 4090 retains a lead in VRAM capacity (24GB vs 16GB) and mature software support for large local models. For most local AI builders running 7B-13B parameter models, the 5080 delivers better performance-per-dollar; however, for 30B+ models or fine-tuning, the 4090’s extra VRAM is non-negotiable.

Quick answer

Which is better for local LLMs, NVIDIA RTX 5080 or NVIDIA RTX 4090?

It depends on your workload. The RTX 5080 offers competitive raw FP16 throughput and improved memory bandwidth efficiency at a lower price, but the RTX 4090 retains a lead in VRAM capacity (24GB vs 16GB) and mature software support for large local models. For most local AI builders running 7B-13B parameter models, the 5080 delivers better performance-per-dollar; however, for 30B+ models or fine-tuning, the 4090’s extra VRAM is non-negotiable.

Quick Verdict

Winner: GPU Architecture

NVIDIA RTX 4090

Winner: CUDA Cores

NVIDIA RTX 4090

Winner: Tensor Cores (4th Gen/5th Gen)

NVIDIA RTX 5080

Overall Pick

It Depends

Side-by-Side Specs

Specification	NVIDIA RTX 5080	NVIDIA RTX 4090
GPU Architecture	Blackwell (GB203)	Ada Lovelace (AD102)
CUDA Cores	10,752	16,384
Tensor Cores (4th Gen/5th Gen)	5th Gen (448)	4th Gen (512)
VRAM Capacity	16GB GDDR7	24GB GDDR6X
Memory Bus Width	256-bit	384-bit
Memory Bandwidth	~960 GB/s	~1,008 GB/s
FP16 (Tensor) TFLOPS	~90 TFLOPS (sparse)	~82.6 TFLOPS (sparse)
FP32 TFLOPS	~45 TFLOPS	~82.6 TFLOPS
INT8 (Tensor) TOPS	~360 TOPS	~660 TOPS
TDP	~300W	450W
Transistor Count	~45.6B	76.3B
Manufacturing Node	TSMC 4NP	TSMC 4N
NVLink Support	No	No (consumer)
Released Price (MSRP)	$999	$1,599
Software Ecosystem (CUDA/cuDNN)	CUDA 12.6+ (limited early support)	CUDA 12.x (mature)

Real Benchmarks

Cross-referenced from our benchmark database , higher is better. Numbers are tokens/sec for LLM workloads, images/min for image workloads.

Real-World Scenarios

If you mostly

Run Llama 3 8B / Mistral 7B daily for coding assistants and summarization, and want lowest cost per token.

Recommend

NVIDIA RTX 5080

The 5080's higher FP16 tensor throughput and lower TDP yield faster token generation per watt for 7B-8B models, and the 16GB VRAM is sufficient for these sizes with 4-bit quantization. You also save $600 upfront vs. the 4090.

If you mostly

Fine-tune or inference on Llama 3 70B or Mixtral 8x22B with 4-bit quantization, requiring 20-24GB VRAM.

Recommend

NVIDIA RTX 4090

The 4090's 24GB VRAM is critical; the 5080's 16GB will force offloading to system memory, severely crippling performance. Even with FP8, large models simply won't fit, making the 4090 the only viable choice.

If you mostly

Experiment with multi-GPU setups (2x 5080 vs 2x 4090) for up to 120B models using tensor parallelism.

Recommend

Either works

Two 5080s cost $2,000 vs $3,200 for two 4090s, and provide 32GB total VRAM with higher aggregate FP16 throughput. However, you lose NVLink and memory bandwidth per card; for equivalent model sizes, the 4090 pair is more reliable due to mature multi-GPU libraries.

Price & Value Analysis

The RTX 5080 at $999 delivers 10-15% better FP16 throughput per dollar than the $1,599 RTX 4090, making it the superior value for smaller local models (up to 13B parameters). The 4090's 50% more VRAM and 300W+ vs 300W TDP give it a better perf/watt ratio for memory-bound workloads, but at 2x the power cost when fully loaded. Total cost of ownership over 3 years favors the 5080 for energy-efficient inference, but the 4090 wins for any scenario requiring >16GB VRAM, where only the 4090 can run without system memory offloading.

NVIDIA RTX 5080

$999

16 GB

360W

NVIDIA RTX 4090

$1,599

24 GB

450W

Where to Buy

NVIDIA RTX 5080

MSRP $99916 GB VRAM360W TDP

View on Amazon ->

NVIDIA RTX 4090

MSRP $1,59924 GB VRAM450W TDP

View on Amazon ->

Final Verdict

For local AI builders who primarily run 7B-13B parameter models with 4-bit quantization, the RTX 5080 is the clear winner due to its lower price, 300W TDP, and minor FP16 throughput advantage. It excels in chatbot, code generation, and RAG tasks where VRAM is not the bottleneck. However, if you intend to load 30B+ parameter models, fine-tune larger checkpoints, or need headroom for future models, the RTX 4090’s 24GB VRAM makes it the only sensible choice, no amount of architectural efficiency compensates for insufficient memory. Ultimately, the decision hinges on model size: the 5080 is the smart buy for mainstream AI workloads, while the 4090 remains the king for heavy lifting. If cost and power efficiency are your priority, go 5080; if VRAM is a hard requirement, go 4090.

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