A Cloud Server GPU for Over 6,000! Nvidia Tesla T10 Review

Tesla T10 GPU-Z

Recently, while browsing China's Xianyu platform, I stumbled upon a unique graphics card – the Tesla T10. Originating from professional data centers, this GPU was designed by NVIDIA specifically for cloud gaming services, primarily used in GeForce NOW servers. These retired cards have now entered the second-hand market, currently selling on Xianyu for about 1,350 RMB (approx. $190 USD). Due to the low price, I purchased two to evaluate their performance.

Hardware Specifications and Performance

  • 16GB GDDR6 Memory
  • 150W TDP Design
  • 單槽全高設計
  • 原廠被動散熱設計
  • PCIe 3.0 x16 介面
  • TU102 Chip
  • Base Clock: 1065 MHz
  • Max Boost Clock: 1590 MHz
  • Memory Clock: 1575 MHz
  • Memory Bus: 256-bit
  • 顯示記憶體:16GB GDDR6

Performance Testing

Test Environment

The testing environments are all VMs.

Linux:

  • Ubuntu 24.04 kernel 6.8.0-51-generic
  • Nvidia Driver: 550.127.08
  • CUDA Version: 12.4
  • CPU: Eypc 7413 16 core vCPU
  • RAM: 16GiB DDR4 3200 MHz ECC REG

Windows:

  • Windows 11 24H2 OS Build 26100.2894
  • Nvidia Driver: 560.81 (AWS Cloud Gaming Driver)
  • CPU: Eypc 7413 16 core vCPU
  • RAM: 16GiB DDR4 3200 MHz ECC REG

Gaming Performance

3DMark Time Spy GPU Score:10092

Tesla T10 Time Spy Score

3DMark Steel Nomad Score:2338

Tesla T10 Steel Nomad Score

Performance is roughly comparable to the RTX 2070 Super and RTX 4060.

AI Performance

Using Llama 3 8B model and testing with llama-bench:

Q4_K Quantized Version (4.58 GiB)

Test Scenario Generation Speed (tokens/s)
512 tokens 62.10
1024 tokens 60.41
4096 tokens 52.43
8192 tokens 41.46

F16 Full Precision Version (14.96 GiB)

Test Scenario Generation Speed (tokens/s)
512 tokens 24.10
1024 tokens 23.85
4096 tokens 22.53

8192 tokens could not be tested due to insufficient memory.

Power Consumption, Cooling, and Thermal Performance

The maximum power consumption of the graphics card is 150W, while the idle power consumption (P8 state) is approximately 18W.

Since it uses entirely passive cooling, the chassis must provide sufficient airflow to manage its temperature.

I am using this graphics card in a Dell PowerEdge R7515 server; at full load, a fan speed of approximately 89% PWM can maintain the card's temperature at 82-83°C.

User Experience

Currently, two T10 cards are installed in the Dell PowerEdge R7515: one is used in a Windows environment as a remote gaming machine, and the other is used in a Linux environment as a GPU computing node for Kubernetes.

In practical applications, it runs quite smoothly whether playing light-to-medium games or running AI models like phi-4. However, the biggest issue lies in thermal control: if the Dell server's third-party PCIe card LFM (Linear Feet per Minute) mode is disabled, the GPU temperature easily reaches 86°C, triggering thermal throttling while the system fans remain at a low speed. Conversely, if LFM mode is enabled, the fans stay at a constant 89% PWM, increasing the overall system power consumption by about 100W, which is not ideal in a colocation environment with high electricity costs. The current workaround is to manually adjust the fan speed only when needed.

At a price of 1,350 RMB (approximately 190 USD), this graphics card offers excellent value for money. I recommend interested enthusiasts consider picking one up.

Pros

  1. Nvidia vGPU Support
  2. 16GB high-capacity VRAM with ECC support
  3. Space-saving single-slot design
  4. Superior AI computing performance

Cons

  1. Requires additional cooling solutions
  2. High temperature control requirements
  3. Power limits restrict chip performance
  4. No display outputs

Conclusion

The Tesla T10 perfectly demonstrates a second life for server-grade hardware in the consumer market. For users who can overcome its thermal limitations and require powerful computing capabilities or VDI solutions, it is a highly attractive choice. It is especially recommended for users with basic hardware knowledge who are willing to invest time in optimization.

Buying Advice

Suitable for:

  • Budget-conscious AI enthusiasts
  • Professional users requiring high VRAM
  • Users needing VDI GPU acceleration solutions

Not suitable for:

  • General consumers
  • 追求即插即用體驗的用戶

References

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