AMD MI50 16GB AI Training | Generated by AI
Here’s the translation of the entire conversation into English:
User: How much does an AMD MI50 16GB cost?
Assistant: The AMD MI50 16GB currently has no official retail pricing and is mainly traded on the secondhand market. Based on listings on secondhand platforms and reseller channels, the price range is roughly:
- Individual sellers on platforms (e.g., Xianyu, forums): Modified or hobbyist-used cards, approximately 500–650 RMB.
- Resellers/wholesalers (e.g., 1688, eBay): Often refurbished or bulk units, approximately 900–1,100 RMB.
⚠️ Key risks before buying:
- Mining/refurbished cards: Most are retired from data centers or used for mining; lifespan is uncertain.
- Cooling issues: The stock blower cooler struggles to keep up—under full load it can exceed 90°C and throttle; you may need to mod it.
- Tinkering required: Many are pure compute cards with no video outputs; you need to flash a Radeon VII BIOS to use them for gaming, and drivers can be finicky.
If you have a specific use case (e.g., AI training vs. gaming), let me know and I can give more tailored advice.
User: 16GB mini DP – can it do AI training?
Assistant: Yes, but it’s a “tinkerer’s path” that requires DIY skills and comes with significant limitations. The MI50 can indeed do AI training, and the 16GB HBM2 VRAM is decent for entry-level work. But the real challenge is the aging software ecosystem—not the mini-DP port.
Hardware capability and the port
- Port purpose: That mini-DP is for display output or general-purpose compute; it has zero impact on AI training performance. Training depends on compute core power and VRAM capacity.
- Decent compute: FP16 performance is 26.5 TFLOPS, with 1 TB/s HBM2 bandwidth—good for small models.
The biggest hurdle: software ecosystem (ROCm)
This is what deters most people—it’s far more complex than hardware interfaces:
- Version ceiling: The MI50 maxes out at ROCm 5.7.2; it does not support the 6.x series (which new frameworks rely on).
- Framework constraints: You’re stuck with older versions—e.g., TensorFlow must be tensorflow-rocm 2.13.0, and it’s only compatible with Python 3.10.
- Officially “end-of-life”: AMD has marked it as EOL and no longer provides active optimization.
Real-world performance and cost
- Inference is decent; training is so-so: Inference offers good value for money—community feedback shows Ollama running 70B quantized models at 15–25 tokens/s. But if you want to train from scratch, the outdated frameworks and poor ecosystem support will make the process painful, and multi-card efficiency is worse than NVIDIA’s.
- Price reference: Most are used pull-out cards in the 600–1,100 RMB range (high risk of mining/refurb). Some claim NOS units sell for $250–280, but you need to verify.
Summary
If you:
- Have Linux experience.
- Don’t mind wrestling with older PyTorch/TensorFlow versions, and are mostly doing inference or verifying existing environments.
- Have a tight budget (treat it as an expensive toy).
Then go for it. But if you want “plug-and-play, efficient training,” consider spending a bit more on an NVIDIA RTX 3060 12GB (for hassle-free ecosystem) or look into the MI100 which supports ROCm 6.x.
If you decide to get this card, would you like me to provide a detailed guide on setting up the ROCm 5.7 and PyTorch environment?
User: AMD Radeon Pro VII 16G – dual-fan design, 16GB VRAM, broken shroud, guaranteed core and VRAM fully functional, no repairs, guaranteed guaranteed guaranteed. Which of the two is better?
Assistant: Based on your description, both cards share the same core (Vega 20) and 16GB HBM2 VRAM—theoretical AI performance is identical. The choice comes down to cooling, connectivity, and risk:
⚖️ Key differences
- Cooling and condition: The stock Radeon Pro VII has a single blower fan at 250W. The “dual-fan” you mentioned is almost certainly a mod. While the seller says the shroud is broken, modded cooling stability and potential pressure on the core are unknowns. The MI50 typically has the stock blower.
- Connectivity and ecosystem: The Radeon Pro VII supports PCIe 4.0 and has 6 mini-DP ports—double the theoretical bandwidth and it can output video. The MI50 is often PCIe 3.0 and has no video outputs—it’s a pure compute card.
- Condition and risk: The seller claims the Radeon Pro VII has “fully functional core/VRAM, no repairs”—this is more reassuring than the mining/server-pull MI50 with unknown history, especially since HBM VRAM failure is basically unrepairable.
💡 Recommendations
- Choose Radeon Pro VII: If you’re new to AI, this is the better pick. It outputs video and has PCIe 4.0—much less hassle. But be very wary of that non-original dual-fan mod; carefully verify whether it’s just a fan/shroud swap and check for PCB warping or repair signs.
- Choose MI50: Only if your budget is extremely tight (~500–600 RMB secondhand), you have Linux experience, and you’re okay with a pure compute card (requires an iGPU or secondary display card).
Final reminder: Both are retired Vega-architecture cards—the software ecosystem (ROCm) isn’t beginner-friendly. If you want to use them for AI, be prepared to spend time fiddling with older drivers and frameworks.
User: Selling an AMD MI50 GPU, 16GB VRAM, with ECC chips, fully functional, no repairs, performance comparable to RTX 3060, ~400k score on Luda Master (Chinese benchmark), 90°C during 10-minute stress test, plug-and-play, suitable for AI training, deep learning, HPC. Priority to local pickup in Guangzhou Tianhe/Panyu, free shipping elsewhere. Just buy it—DM for details. Is 630 RMB a good deal?
Assistant: At 630 RMB (~$87 USD), this is a “fair-to-low” bargain in the current market—absolutely worth it. As long as the core and VRAM are fine, this price is already below the typical Xianyu range (500–650 RMB), making it a great deal.
However, the seller’s claims of “plug-and-play” and “performance comparable to RTX 3060” are exaggerated—you need to set realistic expectations and prepare for some work:
- “Plug-and-play” is false: On Windows, gaming basically equals “plug-in-and-black-screen”—you must tinker with VBIOS flashing (to Radeon VII) and driver signing. The real usability is on Linux (Ubuntu) with ROCm 5.7 for AI. Otherwise, it’s a brick. If you only use Windows, don’t buy it.
- “Performance comparable to RTX 3060” is theoretical only: FP16 (half-precision) is strong, but for FP32 (single-precision) gaming or older frameworks, the actual experience is far behind the RTX 3060 12GB. Also, it has no video outputs—you’ll need an iGPU or a second display card.
- 90°C stress test is “passing grade”: 90°C under full load is very normal for the stock blower; as long as it stays under 95°C, it’s fine. But expect jet-engine noise at full load.
Final advice:
- Go for it (if this is you): If you’re only using Linux for AI and have the patience to set up older environments, 630 RMB for 16GB HBM2 VRAM is unbeatable value.
- Stay away (if this is you): If you just want to plug in and game on Windows or run Stable Diffusion, this card will drive you crazy.
If you decide to buy, the first thing to do is install Ubuntu 20.04—do not use 22.04 or Windows. Want me to send you a set of environment setup instructions specifically for MI50 with ROCm 5.7 and PyTorch?
User: What does “ECC chip” mean?
Assistant: “ECC chips” refer to the VRAM chips on the graphics card that are dedicated to error checking and correction (ECC). Full name: Error Correction Code.
On this MI50, its role is not to boost performance, but to “ensure computed data is accurate.” Here’s a concrete example:
- Without ECC (gaming cards): During AI training, if cosmic radiation or voltage fluctuations cause a 1 to flip to a 0 in VRAM, the training either crashes immediately, or the model becomes “broken” (loss explodes).
- With ECC (MI50): The system detects the error and automatically recalculates the correct 1 using the check code. Training slows down by about 3–5%, but it can stably run for days on large models without failing midway.
Why does the seller specifically mention this? Because it’s a core differentiator between “gaming cards” and “professional compute cards.” The RTX 4090 gaming card lacks ECC, so running 24/7 AI training can be unstable; the MI50 has ECC, proving it’s a legitimate retired server card, built for stability.
Practical impact for your purchase: It’s a great sign—the card is stable. Two things to note:
- ECC is enabled by default; you can turn it off in Linux with
rocm-smifor a slight speed boost (newbies should keep it on for safety). - VRAM failure on this card is catastrophic—if one HBM chip dies, the whole card is bricked. The seller’s mention of “includes ECC” implies they’ve run stress tests, making it more reliable than random secondhand mining cards.
For 630 RMB, getting a 16GB professional card with ECC is a steal—if the core is good. Want me to give you that command to check ECC error counts during stress testing? You can run it as soon as you receive the card to verify the seller’s claims.
User: When was this card (MI50) released?
Assistant: The AMD MI50 was officially released in November 2018—by now (2026), it’s nearly an 8-year-old veteran.
Key dates:
- Announcement: November 6, 2018, at a tech event in San Francisco, touted as “the world’s first 7nm datacenter GPU.”
- Actual availability: Started shipping to datacenter customers in Q1 2019 (around March) .
- Official records: AMD’s website and databases like TechPowerUp list the launch date as November 18, 2018.
Given its release date, picking up this card for 630 RMB is indeed getting a former flagship compute card at rock-bottom prices. However, because it’s an older architecture, Linux drivers (ROCm) are stuck on older versions—which matches what you’ve already learned.