text model · Sarvam · Windows
Can I run Sarvam-M 24B on Nvidia GeForce RTX 4060 Ti (16GB)?
No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
Needs ~16.3 GB even at Q4_K_M, but only ~15 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 1.3 GB: Sarvam-M 24B needs roughly 16.3 GB at Q4_K_M and Nvidia GeForce RTX 4060 Ti (16GB) leaves only about 15 GB usable for a model. The lightest tracked hardware that runs Sarvam-M 24B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Sarvam-M 24B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~16.3 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
Which quant fits
How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 24B
- Q4_K_M size
- 14.3 GB
- Q8_0 size
- 25.1 GB
- Context
- 32k
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Power draw
- ~165 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run Sarvam-M 24B on other hardware
FAQ
Can Nvidia GeForce RTX 4060 Ti (16GB) run Sarvam-M 24B?
No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
How much memory does Sarvam-M 24B need?
Nvidia GeForce RTX 4060 Ti (16GB) does not have enough memory. 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/sarvam-m-24b/nvidia-rtx-4060-ti-16gb) 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.