Radeon PRO W7500
Local inference speed
llama.cpp decode speed (tok/s) - estimated
| Workload | tok/s |
|---|---|
| LLaMA 3 8B Q4 | 30 |
Estimated - Estimated from memory bandwidth (llama.cpp decode) · updated 2026-10-31
Stacks for the Radeon PRO W7500
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Radeon PRO W7500
The Radeon PRO W7500 is an Amd GPU built on the RDNA 3.0 architecture, released 2023-08-03. For running AI locally, the numbers that matter are its 8 GB of GDDR6 and 256 GB/s of memory bandwidth. VRAM decides which models fit at all; bandwidth sets how fast they generate text.
What you can run on 8 GB
At Q4_K_M quantization (the usual local default), 8 GB holds models up to roughly 12B parameters, leaving headroom for context. On this card you can run, among others:
- Gemma 4 12B - 12B parameters
- humanizer - 12B parameters
- Bielik-11B-v3.0-Instruct - 11.2B parameters
- Darwin-9B-NEG - 9.7B parameters
- Ornith-1.5-9B - 9.7B parameters
Larger models need a higher-VRAM card, a second GPU, or CPU offload (which is much slower). Check any specific model with the VRAM calculator, or see the full picture on what can I run.
Local LLM speed (LLaMA 3, llama.cpp)
Single-stream token-generation throughput - estimated from memory bandwidth:
| Model (quant) | Speed on Radeon PRO W7500 |
|---|---|
| Llama 3 8B (Q4_K_M) | 30 tok/s |
| Llama 3 8B (F16) | ✗ won't fit |
| Llama 3 70B (Q4_K_M) | ✗ won't fit |
Because decode is memory-bandwidth bound, the 256 GB/s figure is the best single predictor of chat speed on this card. Estimates are calibrated against measured RTX-40-series cards and are typically within ~15%.
Memory and power
- VRAM: 8 GB GDDR6 (128-bit bus)
- Bandwidth: 256 GB/s
- TDP: 70 W - a 250 W+ power supply is recommended
- Process: 6 nm
- Interface: PCIe 4.0 x8
Quantization and context
Quantization trades a little quality for a lot of VRAM. On 8 GB you can fit roughly a 12B model at Q4_K_M, about a 6B model at the higher-quality Q8, or a smaller model at full FP16. Longer context windows also consume VRAM (the KV cache grows with context length), so leave a few GB of headroom if you plan to use large prompts or many concurrent requests. For most chat and coding use, Q4_K_M on this card is the sweet spot between speed, quality, and the 8 GB budget.
How it compares
Similar cards for local AI, by VRAM and 8B-Q4 speed:
| GPU | VRAM | Bandwidth | Llama 3 8B Q4 |
|---|---|---|---|
| Radeon PRO W7500 | 8 GB | 256 GB/s | 30 tok/s |
| AMD Radeon RX 7600 | 8 GB | 288 GB/s | 33.7 tok/s |
| Arc A530M | 8 GB | 224 GB/s | 26.2 tok/s |
| Arc A550M | 8 GB | 224 GB/s | 26.2 tok/s |
Bottom line
The Radeon PRO W7500 is best for entry-level, budget-llm. With under 12 GB, stick to small quantized models (up to ~7B). If you need more, compare with AMD Radeon RX 7600 and Arc A530M.
Sources
- Specifications: RightNow GPU Database (TechPowerUp data)
- Benchmarks: GPU-Benchmarks-on-LLM-Inference (basis for the bandwidth estimate)
Specs and benchmarks last checked 2026-10-30. Verify current pricing before buying.
Frequently asked
Quick answers to common questions
How much VRAM does the Radeon PRO W7500 have?
The Radeon PRO W7500 has 8 GB of VRAM with 256 GB/s memory bandwidth.
What local AI models can run on the Radeon PRO W7500?
The Radeon PRO W7500 with 8 GB VRAM can run many models depending on quantization. Models up to ~12B params may fit at Q4_K_M. Use our VRAM calculator to check specific models.
Is the Radeon PRO W7500 good for local AI inference?
Radeon PRO W7500 is best for entry-level, budget-llm. Check our hardware directory for alternatives with more VRAM.
Where can I buy the Radeon PRO W7500?
Check our buy links above for the best current prices on Amazon, Newegg, and B&H. Prices vary by retailer and availability.
How does the Radeon PRO W7500 compare to other GPUs?
Radeon PRO W7500 has 8 GB VRAM and 256 GB/s bandwidth. It works best with smaller quantized models. Browse our hardware directory for side-by-side comparisons.
What power supply do I need for the Radeon PRO W7500?
The Radeon PRO W7500 has a TDP of 70W. A standard quality PSU of 650W+ should suffice. Always check the manufacturer's recommendations for your specific build.
Nearby options
Similar hardware and models that fit