The new RTX 5080 16GB vs RTX 5090 32GB local-AI comparison answers the capacity question before the speed question. Both cards are Blackwell devices with CUDA capability 12.0, but the RTX 5090 doubles nominal device memory while also raising reference board power from 360W to 575W.
Why 16GB versus 32GB changes the answer
In Qwen’s official Qwen2.5-32B-Instruct-GGUF repository, Q3_K_M is approximately 15.9GB: that leaves only 0.1GB of file-only margin on RTX 5080 before cache and runtime allocations. Q4_K_M is 19.9GB, so it does not fit wholly on the 16GB card but leaves 12.1GB of nominal file-only margin on RTX 5090. Q6_K is 26.9GB and leaves 5.1GB on RTX 5090; Q8_0 is 34.8GB and fits wholly on neither card.
Those are file boundaries, not peak-memory or speed measurements. Context, cache precision, modality, display use, concurrency, allocator behavior and runtime workspace still matter. Read the 32B VRAM guide or enter the exact workload in the LLM VRAM calculator.
The comparison does not invent a winner
NVIDIA publishes 960GB/s for RTX 5080 and 1,792GB/s for RTX 5090, but bandwidth, core count and peak AI metrics are not universal token-rate or value measurements. A controlled comparison must hold model revision, quantization, runtime and version, kernel path, context, cache, prompt/output profile, batch, concurrency and timing constant while recording placement, peak device memory, CPU offload, wall power and failures.
Continue the decision
Read the full comparison · Run and submit a matched test · Stage a private watchlist
Published October 25, 2026. Sources include NVIDIA’s official RTX 5080 and RTX 5090 specifications and Qwen’s official Qwen2.5-32B-Instruct-GGUF repository. HighVRAM has not independently benchmarked this pair.
Related capacity fork: compare RTX 4090 24GB with RTX 5080 16GB when an older Ada flagship competes with the newer Blackwell tier.