text model · Sarvam · Windows
S Can I run Sarvam-1 2B on Nvidia GeForce RTX 4070 (12GB)?
Yes. Sarvam-1 2B runs on Nvidia GeForce RTX 4070 (12GB) at Q4_K_M (~2.7 GB of ~11 GB usable).
Runs at Q4_K_M using ~2.7 GB of ~11 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~2.7 GB
- Usable on device
- ~11 GB
- Device memory
- 12 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
llama-cli -hf bartowski/sarvam-1-GGUF:Q4_K_M lms get bartowski/sarvam-1-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.). AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 2B
- Q4_K_M size
- 1.55 GB
- Q8_0 size
- 2.69 GB
- Context
- 8k
- Memory
- 12 GB vram
- Usable for weights
- ~11 GB
- Best runtime
- Ollama (CUDA) / vLLM (Linux)
You could also run
Run Sarvam-1 2B on other hardware
FAQ
Can Nvidia GeForce RTX 4070 (12GB) run Sarvam-1 2B?
Yes. Sarvam-1 2B runs on Nvidia GeForce RTX 4070 (12GB) at Q4_K_M (~2.7 GB of ~11 GB usable).
How much memory does Sarvam-1 2B need?
Nvidia GeForce RTX 4070 (12GB) has room to spare. At Q4_K_M the weights are ~1.55 GB; with KV cache and runtime overhead, budget ~2.7 GB at a 4k context.
What is the best tool to run Sarvam-1 2B 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.
Sources
Memory figures are estimates. See methodology.