Can I run LLaVA 1.6 7B on AMD RX 7900 XT?

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Perfect
Yes, you can run this model!
GPU VRAM
20.0GB
Required
14.0GB
Headroom
+6.0GB

VRAM Usage

0GB 70% used 20.0GB

Performance Estimate

Tokens/sec ~63.0
Batch size 4

info Technical Analysis

The AMD RX 7900 XT, featuring 20GB of GDDR6 VRAM and an RDNA 3 architecture, is well-suited for running the LLaVA 1.6 7B vision model. LLaVA 1.6 7B in FP16 precision requires approximately 14GB of VRAM, leaving a comfortable 6GB headroom on the RX 7900 XT. This headroom is beneficial for accommodating larger batch sizes, longer context lengths, and other processes running concurrently on the GPU. While the RX 7900 XT lacks dedicated Tensor Cores found in NVIDIA GPUs, its ample VRAM and 0.8 TB/s memory bandwidth still enable efficient processing for AI inference tasks.

The estimated tokens per second (63) provides a reasonable benchmark for the expected inference speed. However, this can vary based on the specific implementation, optimization techniques employed, and the complexity of the input prompts. The RDNA 3 architecture provides strong compute capabilities that when combined with the available VRAM, allows users to run complex vision models like LLaVA 1.6 7B without encountering memory limitations. The provided batch size estimate of 4 balances throughput and latency, providing a good starting point for experimentation.

lightbulb Recommendation

To maximize performance, leverage inference frameworks like llama.cpp or vLLM, which are known for their optimization capabilities on AMD GPUs. Consider experimenting with quantization techniques, such as INT8 or even smaller, to further reduce VRAM usage and potentially increase inference speed. Monitor GPU utilization and temperature during operation to ensure thermal throttling doesn't impact performance. Because the RX 7900 XT has no tensor cores, consider using software that can leverage the GPU's compute units effectively.

If you encounter performance bottlenecks, try reducing the batch size or context length. Experiment with different optimization flags and compiler options within your chosen inference framework. While the 7900 XT has ample VRAM, the lack of tensor cores may make it slower than a similarly priced NVIDIA card. However, if you already own the 7900 XT, it is more than capable of running LLaVA 1.6 7B.

tune Recommended Settings

Batch_Size
4
Context_Length
4096
Other_Settings
['Enable ROCm optimizations', 'Experiment with different compiler flags', 'Monitor GPU temperature and utilization']
Inference_Framework
llama.cpp or vLLM
Quantization_Suggested
INT8 or Q4_K_M

help Frequently Asked Questions

Is LLaVA 1.6 7B compatible with AMD RX 7900 XT? expand_more
Yes, LLaVA 1.6 7B is fully compatible with the AMD RX 7900 XT, thanks to the GPU's 20GB of VRAM.
What VRAM is needed for LLaVA 1.6 7B? expand_more
LLaVA 1.6 7B requires approximately 14GB of VRAM when using FP16 precision.
How fast will LLaVA 1.6 7B run on AMD RX 7900 XT? expand_more
You can expect approximately 63 tokens per second, but performance may vary depending on optimization techniques and input complexity.