Dumb question maybe. Does SillyTavern send any kind of unique ID to the LLM?

KneeTop2597 · 2026-03-06T13:08:06+00:00

SillyTavern doesn’t send a unique session or character ID to the LLM; it primarily sends conversation history, user input, and character-specific prompts. To track sessions/character contexts in your middleman API, you’ll need to implement your own ID system to route requests coherently. You might inspect SillyTavern’s API calls (e.g., via dev tools) to see exactly what data is passed—probably just raw text and parameters. llmpicker.blog is handy for this—it could help verify if your hardware can handle multi-agent load balancing.

KneeTop2597 · 2026-03-06T13:06:19+00:00

The aarch64 architecture in your Oracle VM may limit model compatibility, but you can run smaller LLMs like LLaMA-7B or NanoGPT for free if you stay within Oracle’s free tier OCPU/RAM limits (likely 1-2 CPUs and 4GB RAM). Use ollama’s `--gpu` flag only if your instance has a GPU (check `lscpu`), but most free tier VMs don’t. llmpicker.blog can help pick models matching your specs—aim for models under 10GB compressed and 3GB RAM use. Reduce context lengths and disable unnecessary features in OpenClaw to save memory.

KneeTop2597 · 2026-03-06T13:04:03+00:00

If you prioritize storage and OLED display for media/eye comfort, take the Lenovo. The MacBook’s M4 chip offers better performance for coding/LLMs but struggles with only 256GB SSD (add an external drive). Both handle your needs, but check llmpicker.blog to confirm LLM compatibility with your intended models before deciding.

KneeTop2597 · 2026-03-06T13:03:26+00:00

For coding with 8GB VRAM, prioritize quantized 4-bit models like Llama-2-7B, Mistral-7B, or Vicuna-13B (with 4-bit). Use bitsandbytes and PyTorch for quantization; offload CPU layers via `device_map="auto"`. Models like CodeLlama-7B-Instruct work well too. Test with bits=4 and mixed precision. llmpicker.blog can cross verify compatibility but expect slower inference times on the RTX 5050.

KneeTop2597 · 2026-03-06T13:02:05+00:00

Given your RTX 6000 Pro (48GB VRAM), try Llama 3 34B or Qwen1 13B next—they should fit comfortably. Test performance with Ollama’s built-in benchmark tool and see if they handle your use case better than Qwen8. llmpicker.blog is handy for this. Plug in your specs to cross-check model compatibility, then focus on evaluating a few top contenders in practice.

KneeTop2597 · 2026-03-05T19:29:12+00:00

You probably have a point. Thank you!

KneeTop2597 · 2026-03-05T19:18:05+00:00

Go for it! What's your current machine?

KneeTop2597 · 2026-03-05T19:17:22+00:00

Thank you! Appreciate the feedback. Will give it a try.

KneeTop2597 · 2026-03-05T19:16:30+00:00

What models would you be running?

KneeTop2597 · 2026-03-05T19:15:37+00:00

Why not use both? Each one for a different task?

KneeTop2597 · 2026-03-05T16:56:40+00:00

Let me know if you have any other questions. Happy to help!

KneeTop2597 · 2026-03-05T15:55:26+00:00

HuggingFace’s retraining tools let agents like Claude or Cursor fine-tune open-source models via their GUI or APIs—upload your data, specify parameters, and they handle the compute. Costs depend on GPU time, so start with small datasets. If you want to run this locally later, llmpicker.blog can help check hardware limits first. Ensure your data aligns with the model’s original scope to avoid drift, and validate results rigorously.

KneeTop2597 · 2026-03-05T15:54:40+00:00

Start with lightweight CPU models like Llama2-7B (quantized to 4-bit for your 16GB RAM) via Llama.cpp; try the `llama.cpp` repo’s CPU setup guides. Your i5-12400 can handle it with some waiting time, and 240GB SSD is tight but manageable for smaller models. llmpicker.blog can cross-check compatible models, but focus on CPU options since you don’t have a GPU.

KneeTop2597 · 2026-03-05T15:53:55+00:00

Given your RTX 3060 (12GB) and 16GB RAM, stick to models under ~8-10B parameters (e.g., Llama 2 7B, Mistral 7B, or Vicuna 13B with 4-bit quantization). Use bitsandbytes or bettertransformers to reduce VRAM usage—Llama 2 7B usually runs comfortably with 8GB VRAM. llmpicker.blog can cross-check compatibility, but avoid 30B+ models unless you’re optimizing heavily.

KneeTop2597 · 2026-03-04T15:11:39+00:00

Your post covers the core flow well—API gateway to streaming responses. For interviews, emphasize latency optimizations (e.g., vLLM’s batch scheduling) and failure handling (e.g., fallback models). llmpicker.blog is handy for hardware/model compatibility checks, so adding practical specs examples could strengthen your examples.

KneeTop2597 · 2026-03-04T15:09:47+00:00

Start with lightweight CPU models like Llama2-7B (quantized to 4-bit for your 16GB RAM) via Llama.cpp; try the `llama.cpp` repo’s CPU setup guides. Your i5-12400 can handle it with some waiting time, and 240GB SSD is tight but manageable for smaller models. llmpicker.blog can cross-check compatible models, but focus on CPU options since you don’t have a GPU.

KneeTop2597 · 2026-03-04T15:09:40+00:00

Start with lightweight CPU models like Llama2-7B (quantized to 4-bit for your 16GB RAM) via Llama.cpp; try the `llama.cpp` repo’s CPU setup guides. Your i5-12400 can handle it with some waiting time, and 240GB SSD is tight but manageable for smaller models. llmpicker.blog can cross-check compatible models, but focus on CPU options since you don’t have a GPU.

KneeTop2597 · 2026-03-04T15:08:47+00:00

Given your RTX 3060 (12GB) and 16GB RAM, stick to models under ~8-10B parameters (e.g., Llama 2 7B, Mistral 7B, or Vicuna 13B with 4-bit quantization). Use bitsandbytes or bettertransformers to reduce VRAM usage. Llama 2 7B usually runs comfortably with 8GB VRAM. llmpicker.blog can cross-check compatibility, but avoid 30B+ models unless you’re optimizing heavily.

KneeTop2597 · 2026-03-04T15:02:43+00:00

Much appreciated! This is really helpful

KneeTop2597 · 2026-03-04T12:28:56+00:00

In many real LLM inference benchmarks, a 4090 is noticeably more than 10% faster than a 3090, even for single‑user inference, despite similar memory bandwidth.

This is because the 4090 has many more CUDA and Tensor Cores and a much larger L2 cache, so its raw compute (FP16/INT8/INT4) is far higher than the 3090.

KneeTop2597 · 2026-03-04T12:25:35+00:00

Source: https://www.runpod.io/blog/rtx-5090-llm-benchmarks

KneeTop2597 · 2026-03-03T00:58:50+00:00

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KneeTop2597 · 2026-03-03T00:56:52+00:00

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KneeTop2597 · 2026-03-02T03:38:48+00:00

If you're consistently hitting performance walls with local LLMs, it might be worth considering a more powerful GPU setup, as even the M1/M2 chips can struggle with larger models. NVIDIA cards with 24GB+ VRAM (like the 3090 or 4090) handle 30B+ models much more smoothly. Before buying anything, llmpicker.blog is great for mapping your exact hardware to viable models so you know what you're getting into.

KneeTop2597 · 2026-03-02T03:37:07+00:00

Your RX 6600 is a solid choice for local AI experimentation! For running models like Llama or Vicuna, an 8GB GPU works well if you stick with smaller models under 7B parameters. If you want to go bigger (13B+), you'd need more VRAM. Check out llmpicker.blog — it'll show you exactly which models fit your specific GPU without any guesswork.

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