Summary of "Can 10 Year-old $5,700 GPU Beat a New $430 GPU? | Tesla P100 Local AI Review"
Product reviewed
Nvidia Tesla P100 (data center accelerator GPU, launched 2016) — tested as a budget option for running local AI models and compared mainly against RTX 5060 Ti.
Key features highlighted (Tesla P100)
- 16GB 2nd-gen HBM (HBM2), described as still relevant for memory-bound workloads compared to mid-range GPUs.
- No display outputs (requires a second GPU for monitor output).
- EPS 12V power connector (not 8-pin) → needs a power adapter (not always included).
- Comes with a passive heat sink only → requires an external blower/fan; 250W TDP means you can’t run it “cheaply.”
Software/runtime considerations
- Latest CUDA no longer supports P100, so older CUDA is required.
- In LM Studio, runtime settings must be adjusted to use older CUDA.
- For newer workflows (e.g., image generation), PyTorch/CUDA downgrades and additional flags are needed.
Setup / user experience notes (clunkiness vs convenience)
The video describes the P100 as not plug-and-play:
- Must use another GPU for display output.
- Requires an EPS 12V adapter (verify before purchase).
- Needs an external fan solution; the reviewer used a 3D-printed/P100-specific setup and manually controlled fan speed.
- Requires older CUDA (and additional tweaks for certain apps).
- Overall: high friction and complexity compared with the consumer RTX card.
Benchmarks and results (P100 vs RTX 5060 Ti)
1) Dense LLM (Qwen 3.6-27B, quant IQ3_XXS)
- P100
- Prompt processing (PP): 127 tokens/sec
- Token generation (TG): 10.37 tokens/sec
- RTX 5060 Ti
- Prompt processing (PP): 388 tokens/sec (~3× faster)
- Token generation (TG): 10.07 tokens/sec (P100 slightly faster)
Interpretation given:
- P100 struggles in compute-bound prompt processing (dated architecture + lack of tensor cores).
- P100 remains competitive in memory-bound token generation thanks to HBM2.
2) Mixture of Experts (Qwen 3.6-35B MoE, quant IQ3_XXS)
- P100
- PP: 295 tokens/sec
- TG: 35 tokens/sec
- RTX 5060 Ti
- PP: 589 tokens/sec (~2× faster; gap shrinks vs dense model)
- TG: 52 tokens/sec (P100 loses this time)
Interpretation given:
- P100 can still be usable, but the advantage becomes less consistent.
3) BF16-type model test (P100 lacks native BF16 support)
- Reviewer runs: Quant 3.54B at BF16 precision
- RTX 5060 Ti: “absolutely destroyed” P100 in PP, >4× faster
- P100: TG only ~10% slower in generation
Interpretation given:
- BF16 performance penalty is severe due to no BF16 hardware, but the reviewer notes real-world use often relies on quantized GGUF instead of BF16, reducing practical impact.
4) Image generation (ComfyUI workflows)
- P100: needs PyTorch/CUDA downgrade + flags (noted as tricky)
- RTX 5060 Ti: much faster
- ~4× faster with Flux client B
- ~2.5× faster with Z image turbo
Interpretation given:
- Compute-heavy generative image tasks are where the P100 becomes especially painful.
Price/value claims
- Original P100 pricing referenced: $5,700 launch; market price up to ~$7,000 at the time.
- Current market: as low as ~$80 on eBay.
- Claimed value strategy:
- At $80, you can buy six P100s to pool VRAM at roughly the same cost as one RTX 5060 Ti.
Pros (as stated)
- Strong performance for memory-bound tasks (HBM2 helps in token generation).
- Budget value: very low acquisition price; potentially good VRAM-per-dollar.
- Can be viable for local quantized LLMs if that’s your primary goal.
Cons (as stated)
- Dated architecture and lack of tensor cores → very poor for compute-bound tasks (especially prompt processing).
- Image generation is painfully slow on P100.
- Clunky setup: no video output, special power connector, requires external cooling hardware.
- Software support limitations: requires older CUDA; additional compatibility work for some tools.
- High power consumption (250W TDP).
Comparisons made
- Repeated direct comparisons against RTX 5060 Ti across multiple workloads:
- Dense LLM: P100 slightly better in TG, much worse in PP.
- MoE LLM: P100 closer in PP; loses in TG.
- BF16: P100 >4× slower in PP; generation only slightly behind.
- Image generation: RTX 5060 Ti 2.5×–4× faster.
Overall verdict / recommendation (concise)
- Recommendation: Buy the Tesla P100 only if you’re specifically targeting cheap local, quantized LLM workloads and you can tolerate setup complexity and slow performance in compute-heavy tasks.
- If you want a fast, plug-and-play experience, the video strongly favors the RTX 5060 Ti.
Unique points mentioned (consolidated list)
- P100 is a 2016 data center GPU.
- 16GB 2nd-gen HBM (HBM2) remains useful vs some mid-range GPUs.
- P100 can be found for ~$80 on eBay (vs much higher historical pricing).
- No display output → needs another GPU for monitor.
- Uses EPS 12V instead of 8-pin → power adapter may be required.
- Comes with passive cooling → requires external blower/fan; 250W TDP needs proper cooling control.
- Requires older CUDA (newer CUDA drops support).
- Dense LLM: PP ~3× slower; TG roughly about equal/slightly better.
- MoE: PP gap narrows (~2× faster on RTX); TG wins for RTX.
- BF16: RTX >4× faster in PP; TG only ~10% slower on P100.
- Image generation: P100 requires more software setup; RTX is ~2.5×–4× faster.
- Final framing: P100 is a budget VRAM / quant LLM option, but not for plug-and-play speed (prompting + images especially slow).
Speakers
- The subtitles indicate one primary speaker/reviewer conducting the setup and benchmarks (no distinct multiple-speaker viewpoints were labeled).
Category
Product Review
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