What is the best local LLM for an RX 7900 XTX?

Qwen 3.6 27B at Q4_K_M is the best general pick on the 24 GB 7900 XTX — about 17 GB and ~40 tokens/sec. For OpenClaw agent work, gpt-oss 20B at Q5 gives cleaner tool calls. The card fits the same model tier as an RTX 3090.

Does the RX 7900 XTX work with Ollama and OpenClaw?

Yes, through ROCm (or Vulkan builds). Ollama supports AMD GPUs, and OpenClaw routes to whatever Ollama runs. Expect occasional driver friction compared with an NVIDIA/CUDA setup — it works well but is a bit less plug-and-play.

RX 7900 XTX vs RTX 3090 for local LLMs?

Both are 24 GB cards that run the same models. The 7900 XTX has higher raw bandwidth and can be cheaper; the 3090 has the mature CUDA ecosystem and the widest tool compatibility. Choose the 7900 XTX if the price gap is real and you are comfortable with ROCm; choose the 3090 for zero-hassle setup.

Can the RX 7900 XTX run a 70B model?

Only at degraded low-bit quants, the same as any 24 GB card. Llama 3.3 70B fits around IQ2_XS (~19 GB) but quality drops sharply. For usable 70B, move to 32 GB+ VRAM or high-unified-memory Apple Silicon.

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Hardware July 6, 2026

Best Local LLM for RX 7900 XTX (2026): 24GB AMD + ROCm Reality Check

The Radeon RX 7900 XTX brings 24 GB of VRAM and ~960 GB/s bandwidth for less than a used 3090 in some markets. The catch is not the model fit — it is the ROCm software stack.

Picking hardware for an OpenClaw host?

Use the local model calculator first, then see our AI training options if you want help matching your workload to the right rig.

Short answer: the 24 GB RX 7900 XTX runs the same model tier as an RTX 3090 — Qwen 3.6 27B at Q4_K_M (~17 GB, ~40 tok/sec) is the pick, with gpt-oss 20B at Q5 for OpenClaw. The honest caveat: you are on ROCm (or Vulkan), not CUDA. Ollama supports it, but expect occasional driver friction and slightly rougher edges than an NVIDIA card.

The VRAM Math

ROCm reality check: for Ollama and llama.cpp, the 7900 XTX works and its 24 GB + high bandwidth are genuinely competitive with a 3090. But if you want zero-hassle setup and the widest tool compatibility, an NVIDIA 24 GB card is still the safer pick. Buy the 7900 XTX if the price gap is real and you are comfortable with ROCm.

What Actually Fits (Model Picks)

Model	Quant	VRAM used	Speed	Notes
Qwen 3.6 27B	Q4_K_M	~17 GB	~40 tok/s	Best general pick (24GB)
gpt-oss 20B	Q5_K_M	~15 GB	~45 tok/s	Best OpenClaw agent pick
Qwen 3.5 9B	Q8_0	~10 GB	~60 tok/s	Fast small model
Llama 3.3 70B	IQ2_XS	~19 GB	~10 tok/s	Fits, but heavily degraded

What You Can’t Run

Llama 3.3 70B at a good quant — like any 24 GB card, only low-bit quants fit and quality suffers.
A frictionless CUDA experience — some tools assume CUDA; on AMD you use ROCm or Vulkan builds and occasionally troubleshoot drivers.
Flash-attention parity in every runtime — support is improving but not universal on RDNA3.

🎮 THE 24 GB AMD CARD — AND THE CUDA ALTERNATIVE

The 7900 XTX is the value 24 GB AMD pick if you are comfortable with ROCm. Prefer mature CUDA/Ollama support at 24 GB? A used RTX 3090 is the safe alternative. Want more AMD VRAM? The Radeon AI PRO R9700 has 32 GB.

7900XFX RX 7900 XTX 24 GB ↗ 3090EVGA RTX 3090 24 GB ↗ R9700XFX Radeon AI PRO R9700 32 GB ↗

OpenClaw Setup

Point OpenClaw at your local model through Ollama:

# pull and run your pick, then set it as the OpenClaw default
ollama pull qwen3:27b
openclaw config set agents.defaults.models.chat "ollama/qwen3:27b"

For agent reliability, prefer a model with clean tool-call output (gpt-oss 20B where it fits) and cap context to what your memory holds. See the tool-calling reliability guide.