More
Submit build

Builds / 2x RTX 5060 8GB

2x RTX 5060 8 GB (16 GB total): Ornith 1.5 35B-A3B Q4_K_M at 53.2 tok/s (llama.cpp, partly in system RAM)

Last updated:

Source: llm-bench.io ↗More source: llm-bench.io ↗📏 How measured: reported by the original source; RigBase re-checked the numbers at that source
GPU(s)
2x RTX 5060 8GB
Amazon · eBay (used) · Newegg
VRAM / unified memory (GB)
16
CPU
not stated
Motherboard
not stated
RAM
not stated
PSU
not stated
Other
Model memory 26.7 GB on the 65K run, more than the 16 GB of VRAM, so part of it sits in system RAM
Reported total cost
not stated
Power
not stated

Measured speed (generation tokens/sec)

ModelQuantEnginetok/sPrompt processing (tok/s)Details
Ornith 1.5 35B-A3BQ4_K_Mllama.cpp b11750 (dd002b91f), 2 GPUs53.2448 (prompt length not stated)context: 131,072 ctx, thinking on📏 llm-bench.io submission, 20 Oct 2026; slower of the two runs listed for this setup (53.2 and 56.5); TTFT 1.375 s (prompt length not stated)
Ornith 1.5 35B-A3BQ4_K_Mllama.cpp b11750 (dd002b91f), 2 GPUs56.5246 (prompt length not stated)
prefill source: llm-bench.io ↗
context: 65,536 ctx, thinking on (8,192 think limit)📏 Second llm-bench.io run the same evening; memory 26.7 GB; TTFT 2.517 s (prompt length not stated)

Prompt processing = prefill speed (how fast the prompt is read) as reported by the source, with the prompt length it was measured at; blank = the source does not report it. est. TTFT @8k is calculated from the measured prefill speed (8,192 ÷ prefill tok/s), shown only when prefill was measured on a prompt of 512+ tokens. It is not a measurement and ignores load time and caching. measured TTFT is the source's own time-to-first-token. Why prefill matters for coding assistants.

llm-bench.io community submissions. Rest of the system not stated.

Source: https://llm-bench.io/benchmarks/cmvh5l4bi01gg01mwik4v0401
https://llm-bench.io/benchmarks/cmvh38ip801fe01mw2y830xim

Blank fields = not stated in the source. We never estimate build data; GPU street prices above are separately sourced and dated.

Embed this result

Copy into a README or blog. Links back to this build. See badge docs (CC BY 4.0 attribution). The SVG does not invent speeds — numbers stay on the build page.

Markdown

[![Measured on RigBase](https://getrigbase.com/badge/llmbench-2x-rtx-5060-ornith-15-35b-a3b-q4km.svg)](https://getrigbase.com/builds/llmbench-2x-rtx-5060-ornith-15-35b-a3b-q4km/)

HTML

<a href="https://getrigbase.com/builds/llmbench-2x-rtx-5060-ornith-15-35b-a3b-q4km/" rel="noopener"><img src="https://getrigbase.com/badge/llmbench-2x-rtx-5060-ornith-15-35b-a3b-q4km.svg" alt="Measured on RigBase" height="20"/></a>

Preview: Measured on RigBase · How to use

Rent before you buy: try this model on a cloud GPU for a few dollars first: Runpod (referral link; sign up with Google to get the new-user bonus)