text model · Sarvam · Windows
S Can I run Sarvam-30B on Nvidia GeForce RTX 4090 (24GB)?
Yes. Sarvam-30B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~21.7 GB of ~23 GB usable).
Fits at Q4_K_M (~21.7 GB of ~23 GB usable) but with little headroom, close other apps.
- Q4_K_M needed
- ~21.7 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 sarvamai/sarvam-30b-gguf:Q4_K_M lms get sarvamai/sarvam-30b-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
- 30B (MoE, 2.4B active)
- Q4_K_M size
- 19.6 GB
- Context
- 64k
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Sarvam-30B on other hardware
FAQ
Can Nvidia GeForce RTX 4090 (24GB) run Sarvam-30B?
Yes. Sarvam-30B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~21.7 GB of ~23 GB usable).
How much memory does Sarvam-30B need?
It is a tight fit on Nvidia GeForce RTX 4090 (24GB). At Q4_K_M the weights are ~19.6 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Sarvam-30B 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.