A Developer’s Critical Review of AI GPUs: Prices, Compute, and Memory
The article analyzes a range of AI graphics cards—from RTX 4090 to Apple M3 Ultra—examining their price, compute performance, memory capacity, and practical suitability, while providing personal judgments on each model’s value for AI workloads.
All objective data were cross‑validated; price samples were taken on 7 June 2026. Subjective opinions are the author’s own and do not constitute purchase advice.
RTX 4090 (24 GB & 48 GB modified)
The flagship card offers a 107 % compute increase over the RTX 3090 Ti. The 24 GB version can be memory‑constrained on some tasks, while the 48 GB modified version removes that bottleneck and even matches the upcoming RTX 5090 (32 GB) in overall capability. Judgment: Top .
RTX 2080 Ti (22 GB modified)
Provides the best price‑performance ratio: 83 RMB/GB memory and 16 RMB/TFLOPS compute, delivering 22 GB memory for about 1 800 RMB. It handles 2K high‑refresh gaming comfortably and remains a strong legacy flagship. Judgment: Top .
RTX 4060 Ti (16 GB)
Costs under 3 000 RMB; two cards give a total of 32 GB memory, enough for many local AI tasks at a total cost of roughly 5 000 RMB. However, its 288 GB/s bandwidth and 88.3 TFLOPS compute are modest. Judgment: Half‑step Top .
RTX 4080 SUPER (32 GB modified)
Initially criticized at 16 GB, the SUPER version adds a custom PCB with eight memory chips to reach 32 GB. Over 2 800 units are listed on a rental platform, placing it in the middle of the market in terms of price, compute, memory, architecture, and stability. Judgment: Top .
RTX 3080 (20 GB modified)
Offers high memory and compute for a low price, becoming a popular entry‑level AI card despite many units having been used for mining. Modified cards mitigate potential reliability issues. Judgment: Between Top and Solid .
RTX 3090 (48 GB modified)
Extremely scarce—only seen on a rental platform’s vGPU‑48G‑350W machines. If mass‑produced, it could cut the RTX 4090 48 GB market price by about 45 % (current price 5 600 RMB, material cost under 9 500 RMB). Currently a legend with no concrete availability. Judgment: Void Card .
RTX PRO 6000 (96 GB)
The latest architecture with the highest compute and memory, regarded as the ultimate AI workstation card. Prices approach 100 000 RMB, translating to roughly 1 000 RMB/GB. The only drawback is its high cost. Judgment: Top .
Apple M3 Ultra (512 GB unified memory)
Features unified memory (512 GB usable as VRAM). A single Mac Studio can host Qwen 3.5‑397B‑A17B‑FP8; four units can run full‑scale DeepSeek‑V4‑Pro/GLM‑5.1. The device is discontinued but still available for about 120 000 RMB (≈ 234 RMB/GB). Drawbacks: limited compute and a weak MLX ecosystem. Judgment: Top .
AMD Ryzen AI Max+ 395 (128 GB unified memory)
AMD’s unified‑memory approach yields a 128 GB CPU‑GPU system priced around 17 000 RMB (≈ 182 RMB/GB), offering compute comparable to the M3 Ultra but lacking CUDA support. Judgment: AMD YES! .
Nvidia DGX Spark & RTX Spark (128 GB unified memory)
Both use the GB10 chip, sharing LPDDR5‑X unified memory and similar compute, memory, and bandwidth characteristics. Their main advantage is the CUDA ecosystem, which may help them capture AI market share despite Windows‑on‑ARM compatibility concerns and modest bandwidth. Judgment: Future War .
Conclusion
There are no inherently bad GPUs—only bad pricing. No product is universally superior; suitability depends on the target scenario. Modified cards illustrate the ongoing memory scarcity. Future trends may include larger memory variants from Nvidia, broader adoption of unified memory, and domestic alternatives gaining traction.
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Network Intelligence Research Center (NIRC)
NIRC is based on the National Key Laboratory of Network and Switching Technology at Beijing University of Posts and Telecommunications. It has built a technology matrix across four AI domains—intelligent cloud networking, natural language processing, computer vision, and machine learning systems—dedicated to solving real‑world problems, creating top‑tier systems, publishing high‑impact papers, and contributing significantly to the rapid advancement of China's network technology.
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