RTX 5080 LLM benchmark
No signed RTX 5080 submissions yet. Check the configuration guidance below, or contribute a reproducible measurement from your own machine.
Specs vendor specs, not measured
- VRAM
- 16 GB
- Memory bandwidth
- 960 GB/s
- Launch date
- 2025-01-30
Sources: nvidia.com, nvidianews.nvidia.com, nvidia.com. Vendor-published figures, accessed 2026-10-14.
Plan memory before choosing a model
The catalog lists 16 GB of VRAM for this hardware entry. Check the capacity of your actual card. Model weights, context cache and runtime overhead must share the available memory. Capacity alone does not establish which model will fit or how fast it will run. Use the memory-fit calculator for your model and context as an estimate, then verify with the intended runtime. No signed RTX 5080 measurement is available yet.
Contribute a signed RTX 5080 benchmark
Run on YOUR hardware to populate this page: pipx install llm-speed && llm-speed bench
$ pipx install llm-speed && llm-speed bench
Community folklore on RTX 5080
13 unverified claims extracted from Reddit/HN comments. Lower trust than signed runs above; every row links to the source.
- communityconfidence 85%
66.00tok/s — qwen3-coder on RTX 5080 via llama.cpp Q4_K_M
our signed data: RTX 5080 · qwen3-coder
“I get around ~66 t/s (16k/32k context, Q4_K_M) with very similar but Notebook hardware: AMD Ryzen 9955HX3D 64GB DDR5-5600 Nvidia RTX 5080 Mobile 16”
- communityconfidence 70%
25.00tok/s — Qwen3-Coder on RTX 5080 via lm-studio
our signed data: RTX 5080 · Qwen3-Coder
“Running Qwen3-Coder 30B at Full 256K Context: 25 tok/s with 96GB RAM + RTX 5080 Hello, I come to share with you my happiness running Qwen3-Coder 30B at its maximum unstretched context (256K).”
- communityconfidence 65%
47.00tok/s — Qwen3-Coder-Next on RTX 5080 IQ3_XXS
our signed data: RTX 5080 · Qwen3-Coder-Next
“del is the biggest one I can run on my 5080 Edit: actually just tried it and I could run Qwen3-Coder-Next UD-IQ3_XXS with 262k context at ~47 t/s which isn't bad!”
- communityconfidence 60%
70.00tok/s — Qwen2.5-14B on RTX 5080 via llama.cpp
our signed data: RTX 5080 · Qwen2.5-14B
“Nice writeup - something's gotta be off with your 5080 tok/sec on Qwen3-30, getting 50tok/sec there but 70t/s on Qwen2.5-14B is funky. What inference backend? You can get \~120tok/sec with a 3090 on Qwen3-30 with llama.cpp and vllm so long as they'”
- communityconfidence 60%
50.00tok/s — Qwen2.5-14B on RTX 5080 via llama.cpp
our signed data: RTX 5080 · Qwen2.5-14B
“Nice writeup - something's gotta be off with your 5080 tok/sec on Qwen3-30, getting 50tok/sec there but 70t/s on Qwen2.5-14B is funky. What inference backend? You can get \~120tok/sec with a 3090 on Qwen3-30 with llama.cpp and vllm”
- communityconfidence 55%
30.60tok/s — Qwen3-Coder-Next on RTX 5080 q8_0
our signed data: RTX 5080 · Qwen3-Coder-Next
“Just tested with my machine: Laptop 9955HX3D, 64GB RAM, RTX 5080 Mobile 16GB. Windows: 23 t/s arch Linux: 32 t/s with CUDA vs 30.6 t/s with Vulkan Run Arguments: "$SERVER_EXE" \ --model "$MODEL_FILE" \ --alias "unsloth/Qwen3-Coder-Next" \ --fit on \ --”
- communityconfidence 55%
32.00tok/s — Qwen3-Coder-Next on RTX 5080 q8_0
our signed data: RTX 5080 · Qwen3-Coder-Next
“Just tested with my machine: Laptop 9955HX3D, 64GB RAM, RTX 5080 Mobile 16GB. Windows: 23 t/s arch Linux: 32 t/s with CUDA vs 30.6 t/s with Vulkan Run Arguments: "$SERVER_EXE" \ --model "$MODEL_FILE" \ --alias "unsloth/Qwen3-Coder-Next" \”
- communityconfidence 55%
23.00tok/s — Qwen3-Coder-Next on RTX 5080 q8_0
our signed data: RTX 5080 · Qwen3-Coder-Next
“Just tested with my machine: Laptop 9955HX3D, 64GB RAM, RTX 5080 Mobile 16GB. Windows: 23 t/s arch Linux: 32 t/s with CUDA vs 30.6 t/s with Vulkan Run Arguments: "$SERVER_EXE" \ --model "$MODEL_FILE" \ --alias "unsloth”
- communityconfidence 45%
50.00tok/s — on RTX 5080 q8_0
our signed data: RTX 5080
“dy in the release builds. # 8 GB VRAM recommendations (u/Qxz3) Use Q4\_K\_M with full expert offload (`-ot "exps=CPU"`): \~7.2 GB VRAM, \~50 tok/s in our tests (on RTX 5080 — your results will vary depending on GPU memory bandwidth). Key flags: `-ctk q8_0 -ctv q8_0` (free lunch…”
- communityconfidence 45%
22.00tok/s — on RTX 5080 q8_0
our signed data: RTX 5080
“--jinja \ -ctk q8_0 \ -ctv q8_0 Happy to answer questions about the setup. Previous model was Qwen3-Next-80B-A3B at \~22 tok/s on the same hardware, so this is a 3.2x speedup with a much more capable model.Qwen3.5-35B-A3B Benchmarks on RTX 5080 16GB”
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