With my NVidia Geforce 3070 card with 8GB of VRAM I can get 40 tokens per second on smallish models such as llama3.1 or qwen 3.8. I use it mostly for NLP processing of game server logs to create match summaries and I use whisper for audio-to-text transcription of videos.

While I do not have enough vram to put large logfiles into the LLM directly I was able to have it create a python script that generates a summary file at the end of each match. I feed the LLM the summary file and it generates color commentary that gets posted to Discord. Here is an example:

[🌟]Color Commentary: A Display of Skill on dod_napoli_b1! [📈]Multi-Player Action: On 08/05/2026 at 19:33:25, the action was intense on dod_napoli_b1, with key moments including the [Loft] capture by Inspector Lag (Allies) and [Axis Barn] capture by THE ROCK/BionicTwin (Allies). [🏆]Impact Players: Allies: Inspector Lag (Kills: 37), [415]Drago (Caps: 10). Axis: The Notorious C.U.M. (Kills: 39), [415]IronHeaD (Caps: 8).

$ollama list NAME ID SIZE MODIFIED
qwen3:8b 500a1f067a9f 5.2 GB 5 months ago
llama3.1:8b 46e0c10c039e 4.9 GB 5 months ago

$ollama run llama3.1:8b

what is the best AI model to run on an Nvidia 3070 with 8gb of vram? The best AI model to run on an NVIDIA GeForce RTX 3070 with 8 GB of VRAM depends on several factors, such as:

1. Specific task or application: Are you interested in tasks like image classification, object detection, natural language processing (NLP), or generative models?
2. Desired level of complexity: Do you want to run small, simple models or larger, more complex ones that require more computational resources?
3. Computational requirements: What’s your expected performance target? Do you need high-throughput, low-latency, or a balance between the two?

Considering these factors and the specifications of your GPU, here are some popular AI model examples suitable for an NVIDIA GeForce RTX 3070 with 8 GB VRAM:

Computer Vision:

1. YOLOv5: A lightweight yet powerful object detection model that’s well-suited for real-time applications.
2. EfficientNet-B3: A compact and efficient image classification model, part of the EfficientNet family.

Natural Language Processing (NLP):

1. BERT-base-uncased: A popular language representation model for various NLP tasks like text classification, sentiment analysis, and question answering.
2. RoBERTa-base: A transformer-based language model that outperforms BERT on several tasks.

Generative Models:

1. StyleGAN2: A state-of-the-art generative adversarial network (GAN) for image synthesis and manipulation.
2. DDPM (Denoising Diffusion Probabilistic Model): A flexible and efficient generative model suitable for various image generation tasks.

Keep in mind that these models have varying computational requirements, memory footprints, and performance characteristics. You may need to experiment with different hyperparameters or architectures to find the best match for your specific needs.

To estimate which models will run smoothly on your hardware, consider the following:

• Memory usage: Make sure the model fits within your GPU’s VRAM (8 GB). Larger models might require downsampling or other memory-saving techniques.
• FLOPS (Floating-Point Operations Per Second): Estimate the computational requirements of each model. The RTX 3070 has a peak FLOPS rating of around 12 TFLOPS, but actual performance may vary depending on the specific workload.

For more detailed estimates and comparisons, I recommend exploring resources like:

• TensorFlow Model Garden: A collection of pre-trained models for various tasks.
• PyTorch Hub: A library of pre-trained models and implementations.
• NVIDIA Deep Learning SDK: Offers a range of tools, including cuDNN-accelerated deep learning primitives.

Feel free to provide more specific requirements or task details if you’d like me to help with model selection!

total duration: 13.256527877s load duration: 71.80054ms prompt eval count: 466 token(s) prompt eval duration: 62.91049ms prompt eval rate: 7407.35 tokens/s eval count: 572 token(s) eval duration: 12.889617619s eval rate: 44.38 tokens/s

No, I prefer using my brain or other things that usually work 100x more efficient without hallucinations and don’t required a shit ton of RAM that are way to expensive to buy right now.

Last night I managed to run Qwen Flash Next iq2 XS using Strata in docker on a VM

I passed through my 3090, 64GB of DDR4, 12 vcores of my 5960x,

Managed 90t/s

I’ll probably try a larger version soon but QFN already seems a lot more capable than 3.6 35b A3B, and runs better than Qwen 3.8 dense in llama.cpp

I use Qwen 3.8 27b Q4 with 100k context (llama.cpp and the Mistral AI harness) on a 24 GB AMD Radeon 7900 XTX and it’s been great. It can handle pretty much anything I throw at it and I can let it work indepedently overnight / over days on long tasks.

I’ve thought about bodging something together with n8n and Tasker so I can just screenshot something or send it a voice note and have it add an event on my calendar.

That being said my server is an 8th gen i3 w/ 12GB of RAM and I’m not willing to leave my desktop running 24/7 so idk if I can run a model good enough to handle this over there. Even if it is a supposedly menial “OCR and call tool” workflow.

I write my own code, skid.

I run Gemma4 12B on my RX9070 XT. it’s not permanently on, I just turn it on when I want to use it. Access it through a browser. Does what I want out of it. Nothing “agentic”.

I self host both at home and at work.

At work (for completion and generation) we have a now quite old A6000 running mainly Qwen 3.6 (though I’m trialling 3.8 from Monday) with OpenClaw as the harness. The use case is mainly triage and pre-work on bugs. We have an agent which can be given an exception report with a bunch of information like stack trace and local variable values and it will go off and gather the required code, order files, and data to analyse the issue and come up with a fair idea of the cause and possible solution. This saves devs and support team time. Also looking at adding a PA agent to this which will be able to read emails and calendars and be able to set up (with approval) meetings and move things around and generally make someone’s life a bit less admin-y without having to hire a full PA role.

Also technically in the AI space we have a fleet of Pis with Hailo AI hats running object and anomaly detection in visible and LWIR spectrums for QC

I don’t want to create an extra post, so I ask here.

Where do you guys get your hardware from, for an affordable price?

I’d like to run at least a 27b model but my hardware is too weak. I’m on a 16GB unified mem, M4. Tried running Bonsai 27b (it’s Qwen architecture) but I had to close all other apps and it is not in a state that I could work with it.

Ollama container, 2070 super, 3-7b, meta and qwen

I use it to ingest tons of news rss, find dupes, summarize, and then post that output to mesh a mesh network.

I also use it for a self hosted mealie to figure out recipe->ingredient.

I also use it for receipts. I translate pdf -> txt, feed the text to qwen, ask it who is the vendor, what is the price, what it a reasonable memo for the receipt, and give a dozen categories, which is best.

Tried running a decent Qwen on a 3060 TI but I only have 16g ram so it kept getting stuck and not completing tasks.

Yes! I’ve got quite a few projects going right now. Probably too many of them to keep track

I am the only user, so it’s relatively easy to run multiple models for what ever task. Ollama is pretty good at balancing which models are working at what time to not bog down they system

I’m running gpt-oss 120 for a general purpose Hermes agent. Out of the box, it was ok. Not very snappy, but it was one of the best ones for orchestrating other agents.

Glm flash (4.something?) does pretty good as well.

I’ve found a hybrid of cloud and local is the best solution. Use local for smaller or more defined tasks. Use cloud for orchestrating, planning, and…fixing the local agents mistakes lol

I’ve got an LLM maintained Obsidian wiki that is written and edited by a few Ollama agents based on chats via my own self hosted matrix instance. I’ve taken it to a point where I’m pretty happy with the results. But I think if I had enough time and energy, I could really dial it in.

One of my favorite use cases for this is when I’m researching topics like UFOs/high strangeness/conspiracy theories. I have my very own LLM maintained Obsidian vault filled with curated notes and links to sources.

Literally, no one cares about this and they think it’s stupid and I’m stupid and I should feel bad for spending time on this or having the audacity of sharing it… they just don’t appreciate the amount of work and knowledge it took to get what I have set up.

I think it’s cool as fuck. I geek out every time that it works.

Oh, forgot to mention I am running it on a Mac studio with 96 gig of unified memory. I think it’s an M3 ultra chip.

Not only do I run and share it. I’ve built an entire service to allow everyone else to do so safely. https://aihorde.net/

Removed by mod

I have an LLM running object detection on a b580 that’s also handling camera rotation in frigate. It’s hanging out 95-120 watts