RTX A4500 Max-Q
RTX A4500 Max-Q
The RTX A4500 Max-Q is an NVIDIA GPU built on the Ampere architecture, released 2022-03-22. For running AI locally, the numbers that matter are its 16 GB of GDDR6 and 448 GB/s of memory bandwidth. VRAM decides which models fit at all; bandwidth sets how fast they generate text.
What you can run on 16 GB
At Q4_K_M quantization (the usual local default), 16 GB holds models up to roughly 25B parameters, leaving headroom for context. On this card you can run, among others:
- gpt-oss-20b - 21.5B parameters
- Qwen2.5-14B-Instruct - 14.8B parameters
- Qwen2.5-Coder-14B-Instruct - 14.8B parameters
- Phi-4 - 14B parameters
- Qwen3 14B - 14B 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 RTX A4500 Max-Q |
|---|---|
| Llama 3 8B (Q4_K_M) | 52.4 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 448 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: 16 GB GDDR6 (256-bit bus)
- Bandwidth: 448 GB/s
- TDP: 80 W
- Process: 8 nm
- Interface: PCIe 4.0 x16
Quantization and context
Quantization trades a little quality for a lot of VRAM. On 16 GB you can fit roughly a 25B model at Q4_K_M, about a 13B 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 16 GB budget.
How it compares
Similar cards for local AI, by VRAM and 8B-Q4 speed:
| GPU | VRAM | Bandwidth | Llama 3 8B Q4 |
|---|---|---|---|
| RTX A4500 Max-Q | 16 GB | 448 GB/s | 52.4 tok/s |
| AMD Radeon RX 7600 XT | 16 GB | 288 GB/s | 33.7 tok/s |
| AMD Radeon RX 7800 XT | 16 GB | 624 GB/s | 73 tok/s |
| AMD Radeon RX 9070 | 16 GB | 512 GB/s | 59.9 tok/s |
Bottom line
The RTX A4500 Max-Q is best for llm-inference, image-gen. Its 16 GB suits 7B-14B models and image generation well. If you need more, compare with AMD Radeon RX 7600 XT and AMD Radeon RX 7800 XT.
Sources
- Specifications: RightNow GPU Database (TechPowerUp data)
- Benchmarks: GPU-Benchmarks-on-LLM-Inference (basis for the bandwidth estimate)
Specs and benchmarks last checked 2026-06-08. Verify current pricing before buying.
Frequently asked
Quick answers to common questions
How much VRAM does the RTX A4500 Max-Q have?
The RTX A4500 Max-Q has 16 GB of VRAM with 448 GB/s memory bandwidth.
What local AI models can run on the RTX A4500 Max-Q?
The RTX A4500 Max-Q with 16 GB VRAM can run many models depending on quantization. Models up to ~24B params may fit at Q4_K_M. Use our VRAM calculator to check specific models.
Is the RTX A4500 Max-Q good for local AI inference?
RTX A4500 Max-Q is best for llm-inference, image-gen. With ample VRAM it handles most open models well.
Where can I buy the RTX A4500 Max-Q?
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 RTX A4500 Max-Q compare to other GPUs?
RTX A4500 Max-Q has 16 GB VRAM and 448 GB/s bandwidth. It is a mid-to-high-range card capable of running most 7B–30B models. Browse our hardware directory for side-by-side comparisons.
What power supply do I need for the RTX A4500 Max-Q?
The RTX A4500 Max-Q has a TDP of 80W. A standard quality PSU of 650W+ should suffice. Always check the manufacturer's recommendations for your specific build.
Nearby options
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