text model · Sarvam · Windows
Can I run Sarvam-1 2B on AMD Ryzen AI Halo (128GB)?
Yes. Sarvam-1 2B runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~2.7 GB of ~96 GB usable).
Runs at Q4_K_M using ~2.7 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 ~93.3 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~2.7 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~120 W
- Electricity / 1M tokens
- ~$0.05
- Pays for itself after
- ~8,887M tok
At ~$0.15/kWh and the estimated ~107 tok/s, a million generated tokens costs about $0.05 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 AMD Ryzen AI Halo (128GB) pays for itself after roughly 8,887 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/sarvam-1-GGUF:Q4_K_M lms get bartowski/sarvam-1-GGUF How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 2B
- Q4_K_M size
- 1.55 GB
- Q8_0 size
- 2.69 GB
- Context
- 8k
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Power draw
- ~120 W
- Best runtime
- llama.cpp (Vulkan/ROCm) / LM Studio
You could also run
Run Sarvam-1 2B on other hardware
FAQ
Can AMD Ryzen AI Halo (128GB) run Sarvam-1 2B?
Yes. Sarvam-1 2B runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~2.7 GB of ~96 GB usable).
How much memory does Sarvam-1 2B need?
AMD Ryzen AI Halo (128GB) has room to spare. At Q4_K_M the weights are ~1.55 GB; with KV cache and runtime overhead, budget ~2.7 GB at a 4k context.
What is the best tool to run Sarvam-1 2B 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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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/sarvam-1-2b/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.