NVIDIA T4 - 16GB usable
70W, single-slot, 16GB, and it runs off slot power in almost any server. Only 320 GB/s, so it favours always-on inference over speed.
Specifications
| Brand | NVIDIA |
|---|---|
| Model | T4 |
| Usable VRAM | 16GB |
| Architecture | Turing |
| CUDA / Stream Processors | 2,560 |
| Memory Bandwidth | 320 GB/s |
| TDP | 70W |
| FP32 TFLOPS | 8.1 |
Current Offers
Prices last updated:
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Lowest tracked offers by retailer
UK · GBP · Weekly since Aug 2026 · Shipping may be extra
- eBay £449 near 90-day low
About these prices
Each line is the lowest offer we tracked from that retailer at weekly snapshots, ending at today's offer. The condition can vary between snapshots (new, used, or unrecorded for older ones). eBay prices exclude shipping.
Weekly offers table
| Date | eBay |
|---|---|
| Today, Nov 3, 2026 | £449.00 |
| Oct 30, 2026 | £449.00 |
| Oct 23, 2026 | £445.30 |
| Oct 9, 2026 | £513.00 |
| Oct 2, 2026 | £539.99 |
| Sep 25, 2026 | £540.00 |
| Sep 18, 2026 | £542.20 |
| Sep 11, 2026 | £542.20 |
| Sep 4, 2026 | £542.20 |
| Sep 1, 2026 | £542.20 |
| Aug 28, 2026 | £542.20 |
| Aug 21, 2026 | £542.20 |
For AI / LLM Use
Good for 14B models. 30B requires aggressive quantization. Slower generation, usable but not snappy. Datacenter card with no display output, may need aftermarket cooling.
What Models Can It Run?
- 14B Q6_K, 30B Q3_K (tight)
- 14B Q4_K_M, 7B full precision
- 7B Q6_K, 14B Q3_K (tight)
- 7B Q4_K_M only
Estimated Performance
Generation: ~24 tokens/sec
Prefill: ~145 tokens/sec
Recommended Quantisations
- Q4_K_M for 14B models
- Q6_K for 7B-8B models
- Q8 for 7B if VRAM allows
Pros & Cons
Pros
- Only 70W TDP: power efficient
- Turing architecture: broad CUDA software support
Cons
- 16GB usable VRAM: may need quantization for 30B+ models
- Moderate memory bandwidth: not the fastest for inference
- No display output: headless only
- May need aftermarket cooling solution
Community Verdict
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