text model · Sarvam · Windows

Can I run Sarvam-30B on AMD Ryzen AI Halo (128GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated

Yes. Sarvam-30B runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~21.7 GB of ~96 GB usable).

Needs ~21.7 GB Device usable ~96 GB

Runs at Q4_K_M using ~21.7 GB of ~96 GB usable. You have room for FP16 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. AMD Ryzen AI Halo (128GB) leaves ~74.3 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed: ~21.7 GB
Usable on device: ~96 GB
Device memory: 128 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~96 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 AMD Ryzen AI Halo (128GB)'s ~96 GB budget; rungs past it are too large.

Run it

Install commands Windows

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf sarvamai/sarvam-30b-gguf:Q4_K_M

LM Studio

$ lms get sarvamai/sarvam-30b-gguf

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: 128 GB unified
Usable for weights: ~96 GB
Power draw: ~120 W
Best runtime: llama.cpp (Vulkan/ROCm) / LM Studio

Best models for AMD Ryzen AI Halo (128GB) →

You could also run

Run Sarvam-30B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)

FAQ

Can AMD Ryzen AI Halo (128GB) run Sarvam-30B?

Yes. Sarvam-30B runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~21.7 GB of ~96 GB usable).

How much memory does Sarvam-30B need?

AMD Ryzen AI Halo (128GB) has room to spare. 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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Markdown

[![Sarvam-30B on AMD Ryzen AI Halo (128GB)](https://localmodel.run/badge/sarvam-30b/amd-ryzen-ai-halo-128gb.svg)](https://localmodel.run/can-i-run/sarvam-30b/amd-ryzen-ai-halo-128gb)

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.