Depends what workload you expect to use it for. Different models for different tasks. Personally for agentic coding on an 8gb GPU I currently use either Groot 19b a3b, cyber tiel 35b a3b or orinth1.5 35b a3b, all in q4_k_m running with llama.cpp with context of 130k. Get about 30 tokens per second. My own benchmarking puts Groot just slightly ahead of sonnet 5, not quite 5.5 though.

I have 8GB VRAM and 32GB RAM, mostly using llama.cpp to run Tiel 35b at Q4, it's ok at writing code in an agent as long as it can compile and run tests to iteratively fix its mistakes.

Though there's been new optimized engines, like Strata, that could maybe allow you to run Qwen Flash Next at decent speeds.

There's also Qwen 4 coming out soon that may give some good new options for us VRAM poors. Just stick to MoE models.

spell check

Depends what your interests are! I can get around 35ish tok/s TG at - c 48,000 of Qwen 3.6-35B-A3B (Q4_K_M) using polystrata on a similar rig (you've got an A1000, I have a T1000).

Which means absolutely nothing to you right now but suffice to say - Polystrata + Qwen 35B is something to try. It's among the "smartest" you can run on your rig.

Personally, for every day use, I like Ling 3.0 Tiny at Q6 or SharpMiniCPM5-2B at Q8. They are stupidly fast, and supported by MCP tools and a bridge I wrote, work as excellent "hey, I'm thinking of X. Can you tell me about it? And what about Y and Z?" or "How does this read?".

I pull out my phone, send them a text message and they text me back.

Think of it like a Google in your pocket, except off line (unless you tell it to go online). Which technically I suppose makes it agentic lol.

(I can show you some examples if you'd like. I keep a set list of 8 tasks I use for benchmarking as I tweak things, as well as some off the wall / out of band queries)

Being that Ling or CPM only take up a sliver of the GPU, I can run them concurrently with other models (like an image generator, text to speech, vision etc) to create a really useful offline house brain, that can roam across my local documents, offline wikipedia (Kiwix) and the like.

It's the combination of things that results in a 1+1=3

Having said that, my AI ladder is

• 90% Ling or minicpm5
• 9% Qwen 3.6-35B
• 1% GLM5.3 Flash via open router.. which costs fractions of a cent for what I need (there are tricks to this but it's probably out of scope to info dump it on you at this stage).

For now, I keep a $20 ChatGPT Plus (separate pools for chat and Codex have been so good) but I am ditching that asap, with the changes coming this month.

HTH. Write back if you need anything else. It's nice to see someone else rocking an A1000 / T1000. Your rig should be significantly faster than mine... which is why the A1000 is still stupidly expensive for a 50W card.

EDIT: just saw you said "CPU is i7-12700". With that RAM, that CPU and that GPU, I'd guess you'll hit 50-60 tok /s with Qwen 3.6-35B using polystrata once you get the GPU / CPU split right.

In other words, congrats and go fuck yourself :) You have a very nice little inference box. And yes, it can do your calendar alerts thing.

You could try KoboldCpp with the All-in-one Starter Pack V2. Or go through their newbie-templates.

Unfortunately, 8GB of VRAM is not going to be enough to run the really interesting stuff. You can do some light image generation, TTS / STT (with Chatterbox and Whisper respectively) and as far as LLMs are concerned, you're looking at Qwen2.5-7B or similar.

The real question on that box is what kind of CPU do you have? 64 gigs of system RAM, you are looking at CPU inference, which is definitely slower than GPU accelerated, but can be workable for Qwen 3.8 or Muse Glimmer. OpenWebUI is a good starting point to have a chat bot. The real question is what are you hoping to be able to do with a local model?

Is that an Ampere card?

I'd sell that hardware and use what you get for it to prepay a DeepSeek API key