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text model · Sarvam · Windows

Can I run Sarvam-30B on Nvidia GeForce RTX 4060 Ti (16GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.

Needs ~21.7 GB Device usable ~15 GB

Needs ~21.7 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 6.7 GB: Sarvam-30B needs roughly 21.7 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-30B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Sarvam-30B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~21.7 GB
Usable on device
~15 GB
Device memory
16 GB
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Which quant fits

Quant ladder vs ~15 GB usable
Q2_K
~14.7 GB
Q3_K_M
~16.8 GB
Q4_K_M
~21.7 GB
Q5_K_M
~23.5 GB
Q6_K
~26.7 GB
Q8_0
~34 GB
FP16
~62.1 GB
The line marks Nvidia GeForce RTX 4060 Ti (16GB)'s ~15 GB budget; rungs past it are too large.

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model Sarvam
Parameters
30B (MoE, 2.4B active)
Q4_K_M size
19.6 GB
Context
64k
Full Sarvam-30B requirements →
Device Windows
Memory
16 GB vram
Usable for weights
~15 GB
Power draw
~165 W
Best runtime
Ollama (CUDA) / llama.cpp CUDA
Best models for Nvidia GeForce RTX 4060 Ti (16GB) →

What you can run instead

Run Sarvam-30B on other hardware

FAQ

Can Nvidia GeForce RTX 4060 Ti (16GB) run Sarvam-30B?

No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.

How much memory does Sarvam-30B need?

Nvidia GeForce RTX 4060 Ti (16GB) does not have enough memory. 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.

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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.