RTX 4080
Mid-tier Ada Lovelace. 16GB VRAM limits model size but strong FP32 at a lower price than 4090.
RTX 4080 Overview
The RTX 4080 is an Ada Lovelace consumer GPU with 16GB of GDDR6X, 97.5 TFLOPS of FP16/BF16 performance, and strong 48.7 TFLOPS FP32 capability. It offers useful acceleration for local experimentation and image generation at a lower tier than the RTX 4090.
Its 717 GB/s bandwidth is adequate for small models that fit wholly in 16GB, but capacity is the primary constraint. A 16GB pool leaves limited room beyond a 7B-class FP16 model once runtime memory is included, so quantization, small batch sizes, or smaller models are often required for language-model work.
The RTX 4080 is best for budget inference, compact fine-tunes, and creative AI workloads that can tolerate consumer reliability. It has neither ECC nor NVLink, ruling it out for pooled-memory configurations and making it a weaker choice for long-running, critical production services.
Memory
Compute Performance
Hardware
Relative Performance
Relative to highest-spec GPU in database
Limitations
Live Cloud PricingOn-demand hourly rates
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Use Case Guidance
LLM Model Size Guidance
Estimates only. Actual capacity depends on context length, KV cache, and framework overhead.
Related Guides
LLM APIs Running on This GPU Class
Providers that serve frontier LLM inference on Ada Lovelace-class hardware.
Related GPUs
Frequently Asked Questions
How much VRAM does the RTX 4080 have?
The RTX 4080 has 16GB of GDDR6X memory with 717 GB/s bandwidth. This enables running models up to approximately 32B parameters at INT4 precision, 16B at INT8, or 8B at FP16.
What is the FP16 performance of the RTX 4080?
The RTX 4080 delivers 97.5 TFLOPS of FP16 performance and 97.5 TFLOPS BF16. INT8 throughput is 195 TOPS. For transformer inference, memory bandwidth (717 GB/s) is often the binding constraint rather than raw TFLOPS.
What is the RTX 4080 best used for?
The RTX 4080 is best suited for: Budget inference, Small model fine-tuning, Cost-efficient serving. Mid-tier Ada Lovelace. 16GB VRAM limits model size but strong FP32 at a lower price than 4090.
What interconnect does the RTX 4080 use?
The RTX 4080 uses PCIe 4.0. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card capacity is the hard ceiling for model size.
What LLM model sizes can the RTX 4080 run?
With 16GB of GDDR6X, the RTX 4080 can run models up to approximately 8B parameters at FP16 (2 bytes/param), 16B at INT8 (1 byte/param), or 32B at INT4/GGUF (0.5 bytes/param). These are estimates — actual capacity depends on context length, KV cache size, and framework overhead. Longer context windows require more KV cache memory, reducing the effective model size that fits.
How does the RTX 4080 compare to the A100 for LLM inference?
The RTX 4080 has 97.5 TFLOPS FP16 vs the A100 80GB's 312 TFLOPS, and 717 GB/s memory bandwidth vs the A100's 2,039 GB/s. For memory-bound autoregressive LLM inference, bandwidth is the primary determinant of tokens-per-second. The A100's higher bandwidth gives it a throughput advantage for large model inference, despite the RTX 4080's lower cost.
What is the power consumption of the RTX 4080?
The RTX 4080 has a TDP (Thermal Design Power) of 320W. This is the maximum sustained power draw under full load. For data center deployments, total rack power consumption is typically 1.2–1.5× the GPU TDP when accounting for CPU, memory, networking, and cooling overhead. At 320W, the RTX 4080 is in the mid-range tier — compatible with standard data center power infrastructure.
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