text model · Sarvam · Windows
S Can I run Sarvam-M 24B on Nvidia GeForce RTX 3090 (24GB)?
Yes. Sarvam-M 24B runs on Nvidia GeForce RTX 3090 (24GB) at Q4_K_M (~16.3 GB of ~23 GB usable).
Runs at Q4_K_M using ~16.3 GB of ~23 GB usable.
- Q4_K_M needed
- ~16.3 GB
- Usable on device
- ~23 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
llama-cli -hf lmstudio-community/sarvam-m-GGUF:Q4_K_M lms get lmstudio-community/sarvam-m-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
- 24B
- Q4_K_M size
- 14.3 GB
- Q8_0 size
- 25.1 GB
- Context
- 32k
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Sarvam-M 24B on other hardware
FAQ
Can Nvidia GeForce RTX 3090 (24GB) run Sarvam-M 24B?
Yes. Sarvam-M 24B runs on Nvidia GeForce RTX 3090 (24GB) at Q4_K_M (~16.3 GB of ~23 GB usable).
How much memory does Sarvam-M 24B need?
Nvidia GeForce RTX 3090 (24GB) has room to spare. At Q4_K_M the weights are ~14.3 GB; with KV cache and runtime overhead, budget ~16.3 GB at a 4k context.
What is the best tool to run Sarvam-M 24B 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.