text model · RNJ · Windows
Can I run RNJ-1 8B on Nvidia GeForce RTX 3060 Ti (8GB)?
Yes. RNJ-1 8B runs on Nvidia GeForce RTX 3060 Ti (8GB) at Q4_K_M (~6.3 GB of ~7 GB usable).
Fits at Q4_K_M (~6.3 GB of ~7 GB usable) but with little headroom. Close other apps; a smaller context frees a few hundred MB.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 3060 Ti (8GB) leaves ~0.7 GB of headroom.
- Q4_K_M needed
- ~6.3 GB
- Usable on device
- ~7 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~200 W
- Electricity / 1M tokens
- ~$0.14
- Pays for itself after
- ~1,108M tok
At ~$0.15/kWh and the estimated ~61 tok/s, a million generated tokens costs about $0.14 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$399 Nvidia GeForce RTX 3060 Ti (8GB) pays for itself after roughly 1,108 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run rnj-1:8b llama-cli -hf unsloth/rnj-1-instruct-GGUF:Q4_K_M lms get unsloth/rnj-1-instruct-GGUF - Parameters
- 8B
- Q4_K_M size
- 4.76 GB
- Q8_0 size
- 8.23 GB
- Context
- 32k
- Ollama tag
- rnj-1:8b
- Memory
- 8 GB vram
- Usable for weights
- ~7 GB
- Power draw
- ~200 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
You could also run
Run RNJ-1 8B on other hardware
FAQ
Can Nvidia GeForce RTX 3060 Ti (8GB) run RNJ-1 8B?
Yes. RNJ-1 8B runs on Nvidia GeForce RTX 3060 Ti (8GB) at Q4_K_M (~6.3 GB of ~7 GB usable).
How much memory does RNJ-1 8B need?
It is a tight fit on Nvidia GeForce RTX 3060 Ti (8GB). At Q4_K_M the weights are ~4.76 GB; with KV cache and runtime overhead, budget ~6.3 GB at a 4k context.
What is the best tool to run RNJ-1 8B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/rnj-1-8b/nvidia-rtx-3060-ti-8gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.