AMD Just Redrew the Local AI Map – vLLM’s Ryzen AI Support Changes Everything

For years, NVIDIA has dominated the local AI inference space with CUDA’s walled garden, leaving AMD users with awkward workarounds and limited choices. That era just ended. The recent vLLM PR #25908 adding official support for AMD’s Ryzen AI MAX 395 and AI 300 series (gfx1150/1151) isn’t just another GitHub merge, it’s a seismic shift in the local AI hardware landscape.

What Just Changed: The Technical Lowdown

The implementation was surprisingly straightforward, just adding gfx1150 and gfx1151 to the supported architectures in CMakeLists.txt and Dockerfile.rocm_base. But the impact is anything but simple. As one developer noted early in the process, “This is big :O VLLM is the go to standard for deploying and testing, great to see that support!”

The critical update came when gemini-code-assist bot flagged a potential build-breaking oversight: the __HIP__GFX11__ macro definition needed to include the new architectures. The fix required updating:

#if defined(__HIPCC__) && (defined(__gfx1100__) || defined(__gfx1101__))
  #define __HIP__GFX11__
#endif

To include support for the new Ryzen AI chips:

#if defined(__HIPCC__) && (defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1150__) || defined(__gfx1151__))
  #define __HIP__GFX11__
#endif

Why This Matters: Breaking NVIDIA’s Local AI Monopoly

Until now, if you wanted serious local inference performance, your choices were essentially NVIDIA or GTFO. AMD’s ROCm stack has been playing catch-up for years, but with vLLM, the modern standard for production LLM serving, adding official support, the playing field just leveled significantly.

The implications are staggering for developers. As cafedude noted, “As a strix halo user, definitely glad to see this. Some of the newer models released recently only work on vLLM (IIRC there was a diffusion model released the other day that was only working with vLLM).”

The Multi-GPU Dream: Combining Ryzen AI Chips

One of the most exciting revelations comes from users pushing the boundaries. One developer commented they have “two 128GB 395s” and wondered about combining them for massive models. The response was clear: it’s possible via USB4 connections, opening up possibilities for truly consumer-grade multi-GPU AI setups.

This approach could enable running models that dwarf what’s possible on even high-end consumer NVIDIA cards. As the discussion continued: “Most consumer setups tap out at 24GB memory, I’m looking for models literally 10x that if they scale with competence.”

The Reality Check: Current Limitations and Workarounds

The official support isn’t without its growing pains. Early adopters report that FlashAttention doesn’t work automatically yet, and AITER requires “cherrypicking patches.” The recommendation from AMD engineers is to use ROCm 6.4.4 for now, with proper support coming in upcoming 7.0.x releases.

Local LLM Hosting: Complete 2025 Guide - Ollama, vLLM, LocalAI, Jan, LM Studio & More — Local LLM Hosting: Complete 2025 Guide – Ollama, vLLM, LocalAI, Jan, LM Studio & More

There’s also ongoing work to optimize performance for specific model architectures. As highlighted in Issue #29290, “Kimi v2 thinking num_ heads is 64, but aiter mla backend only support num_heads==8 and 128”, indicating there’s still fine-tuning needed for optimal performance across all model types.

The Bigger Picture: What This Means for Local AI Development

This move represents more than just another GPU support addition. It signifies AMD is serious about competing in the AI inference space, and vLLM’s maintainers recognize that supporting alternative hardware ecosystems benefits everyone.

The timing couldn’t be more strategic. With NVIDIA’s consumer GPU pricing reaching astronomical levels and supply constraints making high-end AI cards scarce, AMD’s aggressive push into the local inference market provides a desperately needed alternative. Companies building local AI applications can now seriously consider AMD-based systems for cost-effective scaling.

Getting Started: Practical Deployment

For developers ready to jump in, the recommended path currently involves using the preview Docker image hyoon11/vllm-dev:20250924_6.4_129_py3.12_torch2.8_triton3.4_stx_upstream_5438967_ubuntu24.04 based on ROCm 6.4.4. While it’s labeled as a “preview version”, the feedback from early adopters suggests it’s stable enough for development and testing purposes.

The key advantage here is that AMD’s approach, particularly with the Strix Halo and similar architectures, integrates NPU acceleration alongside traditional GPU compute, potentially offering more balanced power consumption for AI workloads compared to brute-force GPU approaches.

The Future Looks Multi-Vendor

This support marks a turning point. For years, the AI development ecosystem has been constrained by NVIDIA’s hardware dominance. With vLLM, the framework powering some of the largest LLM deployments, now officially supporting AMD’s latest hardware, we’re witnessing the beginning of a genuinely competitive local AI hardware market.

The downstream effects are obvious: more hardware options means lower costs, more innovation, and ultimately, faster progress in making local AI accessible to everyone. While NVIDIA isn’t going anywhere, they’re no longer the only game in town for serious local inference work.

As one developer perfectly summarized the community sentiment: “I would give it few weeks, unless you have some time to kill.” The wait for truly open local AI hardware alternatives just got substantially shorter.