Question 1

What AI models can run on Gaudi HL-205?

Accepted Answer

The Gaudi HL-205 (32 GB VRAM) can run models including Llama-2-7b-chat, Llama-3.2-1B-Instruct-QLORA_INT4_EO8, MediPhi-Instruct, Llama-3.2-3B-Instruct-SpinQuant_INT4_EO8, Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8 and more — 10 compatible models indexed so far. Compatible models are determined by whether the model's memory footprint fits within the GPU's 32 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for Gaudi HL-205?

Accepted Answer

We don't currently have cloud pricing data for the Gaudi HL-205. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is Gaudi HL-205 good for fine-tuning?

Accepted Answer

The Gaudi HL-205 can handle fine-tuning with some constraints. With 32 GB of VRAM, it's suitable for LoRA/QLoRA fine-tuning of models up to ~7B parameters. Full fine-tuning is limited to smaller models (~1-3B parameters). Techniques like gradient checkpointing and mixed precision help maximize what fits.

Question 4

How much VRAM does Gaudi HL-205 have?

Accepted Answer

The Gaudi HL-205 has 32 GB of VRAM (HBM2). It also offers 1000 GB/s memory bandwidth.

Question 5

What quantization formats work on Gaudi HL-205?

Accepted Answer

The Gaudi HL-205 supports the following quantization formats: FP16 (full precision), Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~7B parameters. Q4 quantization lets models up to ~108B parameters (by VRAM estimate) fit on the Gaudi HL-205.

Gaudi HL-205

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	32 GB
Memory type	HBM2
Bandwidth	1000 GB/s
FP16 TFLOPS	—
FP8 support	No