RTX 5070 Ti LLM benchmark
No signed RTX 5070 Ti submissions yet. Check the configuration guidance below, or contribute a reproducible measurement from your own machine.
Specs vendor specs, not measured
- VRAM
- 16 GB
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 5070 Ti measurement is available yet.
Contribute a signed RTX 5070 Ti 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 5070 Ti
9 unverified claims extracted from Reddit/HN comments. Lower trust than signed runs above; every row links to the source.
- communityconfidence 75%
42.00tok/s — Qwen3-30B-A3B on RTX 5070 Ti via ollama IQ4_XS
our signed data: RTX 5070 Ti · Qwen3-30B-A3B
“(hardware is a RTX 5070Ti 16GB GPU dedicated to this). I was going to test Qwen3-30B-A3B-Instruct-2507-GGUF:IQ4_XS which I can get about 42 tok/s using a RTX 5070Ti.”
- communityconfidence 55%
43.88tok/s — Qwen3-Coder-Next on RTX 5070 Ti IQ4_XS
our signed data: RTX 5070 Ti · Qwen3-Coder-Next
“n3-Coder-Next-IQ4_XS` For scaffolding a small Rust Project this gives me: - Prompt Processing: `154.55 tokens/s` - Token Generation: `43.88 tokens/s` I have been previously using llama with -ngl 12-14 (depending on how much free VRAM I have), which gave me only around 14 t…”
- communityconfidence 55%
154.6tok/s — Qwen3-Coder-Next on RTX 5070 Ti IQ4_XS
our signed data: RTX 5070 Ti · Qwen3-Coder-Next
“- RTX 5070 Ti 16GB I'm running `Qwen3-Coder-Next-IQ4_XS` For scaffolding a small Rust Project this gives me: - Prompt Processing: `154.55 tokens/s` - Token Generation: `43.88 tokens/s` I have been previously using llama with -ngl 12-14 (depending on how much free VRAM I h…”
- communityconfidence 50%
79.00tok/s — on RTX 5070 Ti via llama.cpp
our signed data: RTX 5070 Ti
“RTX 5070 Ti + 9800X3D running Qwen3.6-35B-A3B at 79 t/s with 128K context, the --n-cpu-moe flag is the most important part. Spent an evening dialing in Qwen3.6-35B-A3B on consumer hardware. Fun”
- communityconfidence 45%
69.55tok/s — on RTX 5070 Ti q8_0
our signed data: RTX 5070 Ti
“ms / 21 tokens ( 5.47 ms per token, 182.86 tokens per second) eval time = 4241.54 ms / 295 tokens ( 14.38 ms per token, 69.55 tokens per second) total time = 4356.38 ms / 316 tokens llama_memory_breakdown_print: | memory breakdown [MiB] | total f…”
- communityconfidence 45%
70.00tok/s — on RTX 5070 Ti Q3_K_M
our signed data: RTX 5070 Ti
“ending) Memory and CPU changes (efficiency vs performance threads, as an example) weren't letting me run the Q4, but going to Q3 got me to 70 t/s - .\llama-server.exe ^ -hf unsloth/Qwen3.6-35B-A3B-GGUF ^ -m Qwen3.6-35B-A3B-UD-Q3_K_M.gguf ^ --device CUDA0 ^”
- communityconfidence 45%
24.00tok/s — on RTX 5070 Ti q8_0
our signed data: RTX 5070 Ti
“f --host 0.0.0.0 --port 8080 -ctk q8_0 -ctv q8_0 --fit on -ot "exps=CPU" --flash-attn on --jinja -c 48000 -t 20 --fit on -ot "exps=CPU" 24tps llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | lla…”
- communityconfidence 45%
13.00tok/s — on RTX 5070 Ti q8_0
our signed data: RTX 5070 Ti
“./e/Qwen3.5-35B-A3B-Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ctk q8_0 -ctv q8_0 --fit on --flash-attn on --jinja -c 48000 -t 20 --fit on 13 tps llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | l…”
- communityconfidence 45%
33.00tok/s — on RTX 5070 Ti q8_0
our signed data: RTX 5070 Ti
“f --host 0.0.0.0 --port 8080 -ctk q8_0 -ctv q8_0 -ngl 999 --n-cpu-moe 24 --flash-attn on --jinja -c 48000 -t 20 -ngl 999 --n-cpu-moe 24 33tps llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | ll…”
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