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 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.
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.
See Also
- AMD R9700 vs RTX 3090 for Local LLMs — the 32GB AMD workstation option
- Best Local LLM for RTX 3090 (24GB) — the CUDA 24GB alternative
- Can a 24GB GPU Run a 70B Local LLM? — why 70B needs more than 24GB
- Best Local LLM by GPU (hub)
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