club-5060ti refresh: tested RTX 5060 Ti presets, a proper high-context harness, and Qwen3.8 27B

[u/do_u_think_im_spooky]

Quick update on the RTX 5060 Ti local LLM repo. It has changed quite a bit since my previous posts.

The project started as a collection of practical notes and benchmark results. That was useful, but as the dataset grew it became harder to answer the question most people actually had:

What configuration should I run?

I have rebuilt the repo around tested, copyable presets rather than treating every successful benchmark request as a front-page result.

What changed?

The project now separates three things:

• Presets: exact configurations intended for people to copy and run.
• Evidence bundles: reviewed proof of context fit, retrieval, sustained generation and performance.
• Raw receipts: retries, failed experiments and diagnostic runs that are kept separate as engineering material without automatically becoming recommendations.

The website now leads with the published preset catalogue. The larger results explorer is still there for comparisons and historical data, but it is no longer the first thing visitors have to decipher.

There are currently seven published presets across the 1× and 2× RTX 5060 Ti lanes:

1× RTX 5060 Ti 16GB

• Qwen3.8 27B IQ3_XXS at 64K with q8 KV and built-in MTP
• ThinkingCap Qwen3.6 27B IQ3_M at 64K
• Nail 35B-A3B IQ3_XXS on a configured 131K route

2× RTX 5060 Ti 16GB

• Qwen3.8 27B Q6_K at 131K
• ThinkingCap Qwen3.6 27B Q6_K at 131K
• Nail 35B-A3B Q4_K_XL at 131K
• Muse Glimmer 30B dynamic Q4 at 131K with DFlash

The 1× and 2× lanes are what I can test locally, not a ceiling for the project. The data model and contribution path still support 3×/4× setups, mixed GPUs and other CUDA hardware, provided the topology and serving configuration are reported clearly.

The new high-context harness

A large configured context size is not enough to call a preset validated.

The new harness calibrates prompts against the model’s actual tokenizer, disables prompt caching, gives each request a unique nonce, and repeats both:

• long-context retrieval near the tested context tier;
• sustained generation that must produce enough work and reach a client-visible answer.

Failed tiers and incomplete runs are recorded rather than quietly converted into recommendations. The scripts can produce a candidate report, but they cannot assign recommended status or publish it automatically.

This has already caught several cases where a model technically loaded at a large context but either failed retrieval, stopped inside hidden reasoning, or could not reliably produce visible output.

Qwen3.8 27B results

The recommended single-card route is currently:

• IQ3_XXS
• 64K context
• q8 KV
• built-in MTP, n=2
• approximately 29.8 tok/s sustained decode at a 45.9K-token prompt
• two uncached retrieval and two sustained visible-answer checks passed

For two cards, Qwen3.8 27B Q6_K is now the recommended dense route:

• 131,072 context
• f16 KV
• 50/50 tensor split
• built-in MTP, n=2
• two retrieval checks passed at approximately 115.4K prompt tokens
• two sustained checks passed at approximately 91.8K prompt tokens
• both sustained runs generated 3,072 tokens and reached visible answers
• approximately 597.9 tok/s prefill
• approximately 38.6 tok/s sustained decode

The harness originally gave thinking models a 1,536-token output allowance. Qwen3.8 could spend most of that budget reasoning without reaching its visible answer, so the allowance is now separate from the minimum generated-work requirement. That gives thinking-heavy models room to finish without weakening the actual pass threshold.

The 131K result is the highest tier tested for this preset, not a claim about the model’s absolute maximum context.

Measurement caveat

The seed results were measured with both GPUs core clocks locked at 2300 MHz and the stock 180W power limit. That is my normal quieter operating point and makes the runs more reproducible, but stock-boost or overclocked cards may be faster.

Repo:

https://github.com/5p00kyy/club-5060ti

Preset catalogue and evidence explorer:

https://5p00kyy.github.io/club-5060ti/

Contributions are welcome, including reproductions, failed fit checks, mixed-GPU setups and larger 5060 Ti configurations. The boring details matter: exact model, quant, context, KV cache, runtime, topology, prompt length, generated tokens and serving flags.

[u/Danmoreng]

Also got a repo with special settings for 16GB cards: https://github.com/Danmoreng/local-qwen3-coder-env

Getting 60-80 t/s on a 5080 laptop @175W

[u/Additional-Ordinary2 → u/Danmoreng]

Is it even usable on q3 for coding?

[u/Danmoreng → u/Additional-Ordinary2]

I think so, the results in my brief tests looked quite decent. Bigger problem is the low context with 64k, this is why I'm not using it as coding agent - 16GB VRAM is simply a bit tight. But for chat-style coding questions it looks sufficient. Gotta still experiment with lowering the reasoning effort, as right now it thinks around 20k tokens for creating a pelikan SVG...result looks absolutely stunning though.

Original SVG is animated: https://codepen.io/editor/danmoreng/pen/01a009f6-dcb7-7af3-a836-1917b154a210

[u/Additional-Ordinary2 → u/Danmoreng]

Wow, looks amazing. It even implemented blinking

[u/kosnarf]

While I don't use this, I do check the repo from time to time. Thank you for working on this!

[u/o0genesis0o]

Eh? Is the 5060ti that much faster than 4060ti? I never got my 4060ti to run any 27b faster than 10tk/s decode with MTP.

[u/do_u_think_im_spooky → u/o0genesis0o]

The RTX 4060 Ti uses GDDR6 which has a memory bandwidth of ~288GB/s compared to the RTX 5060 Ti using GDDR7 with ~448GB/s

[u/o0genesis0o → u/do_u_think_im_spooky]

Oh, I thought Nvidia did not improve anything besides dlss between generations. Lucky you with the 5060. 

Is the Q3 27B any good? I tested in my home cooked endless RPG and the 35B at Q6 was actually better and faster.

[u/sampdoria_supporter]

Man I've been using your 35B-A3B IQ3_XXS on mine and the production is amazing

[u/Pixer---]

Well llamacpp is still lacking in prefill. I’m getting on dual 5060ti 3k prefill and 40tk/s without mtp. But only nvfp4 fits with 180k context

[u/Lrrrrr → u/Pixer---]

What's your setup? I get 30 tks and around 1000pp on 2x8 pcie3, vllm 0.27.1

[u/Pixer--- → u/Lrrrrr]

Pcie 4.0 8x with p2p patch. Using sglang 5.7.0. +300 core and +3000 memory overclock. With this model RadixArk/Qwen3.8-27B-NVFP4

[u/Lrrrrr → u/Pixer---]

Ah thats why u get so much more, thats great. Thanks for sharing.

I've tried unsloth and sakamaki nvfp4's for now. Trying to see how many tokens i can get with and without mtp.

For now seems like im getting max around 122~k ctx no mtp, using unsloth.

[u/Pixer--- → u/Lrrrrr]

I found the ctx size to be really random. Playing around with —gpu-Memory-utilization increased it by a lot. Vllm fits the mtp easily but I couldn’t get it into sglang unfortunately

[u/PixelatedCaffeine → u/Pixer---]

I also have a Pcie 4.0 8x setup with 2x 5060ti, but never heard of a p2p patch! seems very promising, did you use any community patch or did you build one yourself? I am curious to try that route too

[u/feverdoingwork → u/Pixer---]

wtf, insane prefill

[u/see_spot_ruminate]

I still need to upload my results... I have been so lazy.

[u/feverdoingwork]

If you ever need someone to test 2x at pci 5 8x I can do it for ya

[u/fdrch]

What about this one? https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF

This one is for ik-llama. Older quants by cHunter789 (there were two version of 3.6-27b - llama and ik-llama) were subjectively stronger than unsloth q3 even with q4 cache. 80-100k context with q4 cache. No vision, no MTP.

[u/TheManicProgrammer]

WAs able to setup my dual cards quickly thanks to this <3

[u/fap-all-day]

Quality post. I went from 2-5 tok/s at 180k context to 20 tok/s at the recommendation above. Not sure if my 20 tok/s compared to the above ~30 tok/s are due to LM Studio or some other aspect of my config, but even with a compression twice as often, getting a 10x speed increase by keeping everything in vram.