Is the $80 Tesla M40 Still Viable for AI in 2025?
Last updated: December 2025
The NVIDIA Tesla M40 is legendary in budget AI circles. At $80-100 on eBay, it offers 24GB of VRAM for less than a nice dinner. That's enough memory to run 30B+ parameter models that would be impossible on consumer cards.
But should you actually buy one? Let's break it down honestly.
The Specs
| VRAM | 24GB GDDR5 |
|---|---|
| Architecture | Maxwell (2015) |
| CUDA Cores | 3072 |
| Memory Bandwidth | 288 GB/s |
| TDP | 250W |
| Compute | FP32 only (7 TFLOPS) |
| Display Output | None |
| Cooling | Passive (requires airflow) |
The Good
Why people love the M40:
- Cheapest 24GB option: Nothing else comes close at this price point. Period.
- Actually runs big models: You can load Qwen 32B, Llama 30B, and other models that choke on 16GB cards.
- Readily available: eBay is flooded with decommissioned datacenter units.
- It works: Despite the age, driver support exists and llama.cpp will run on it.
The Bad
The tradeoffs you're accepting:
- No modern optimizations: Maxwell doesn't support Flash Attention, FP16 tensor ops, or INT8 quantization acceleration. You're stuck with FP32.
- Slow by modern standards: ~7 TFLOPS FP32 vs 142 TFLOPS FP16 on an RTX 3090. Expect 3-5 tokens/second on 30B models.
- Passive cooling nightmare: This is a datacenter card. No fans. You need a server chassis with high-CFM airflow or an aftermarket cooling solution.
- No display output: Can't use it as your main GPU. Need a separate card for video.
- Power hungry: 250W TDP for relatively modest performance.
- Software ceiling: Some newer inference engines don't fully support Maxwell. ExLlama, for instance, requires Pascal or newer.
Real-World Performance
Let's be honest about what you're getting:
- Llama 2 7B Q4: ~15-20 tokens/second
- Llama 2 13B Q4: ~8-12 tokens/second
- Qwen 32B Q4: ~3-5 tokens/second
These numbers are rough estimates. The M40's lack of FP16/tensor acceleration means inference engines can't use their fastest code paths. You're essentially leaving performance on the table.
The Cooling Problem
This deserves its own section. The M40 is a passive heatsink designed for server racks with 10,000+ RPM fans blowing directly across it. In a standard PC case, it will thermal throttle or shut down within minutes under load.
Your options:
- Server chassis: The "proper" solution. Loud and expensive.
- Aftermarket cooler: Arctic Accelero or similar. Voids any remaining warranty, requires modification.
- Zip-tie fans: The budget approach. Strap 2-3 120mm fans directly to the heatsink. Ugly but functional.
Who Should Buy the M40?
The M40 makes sense if:
- You're on an extremely tight budget (sub-$100)
- You already have a server or don't mind DIY cooling
- You want to experiment with large models, not run them in production
- Speed isn't critical (you're fine waiting 30+ seconds for responses)
The Verdict
The Tesla M40 is the absolute cheapest way to get 24GB of VRAM. If you have $80 and some patience for DIY cooling, it will technically run 30B models.
But "technically runs" and "good experience" are different things. The lack of modern instruction sets (FP16, INT8, Flash Attention) creates a performance ceiling that makes every interaction feel sluggish.
Consider the Tesla P40 Instead
For about $60 more, the Tesla P40 offers:
- Same 24GB VRAM
- Pascal architecture with FP16 support
- 2x faster real-world inference
- Better software compatibility
- Same cooling requirements
The P40 hits the sweet spot of price and usability. It's what we actually recommend for budget 24GB builds.
Still Want an M40?
We're not tracking M40 prices on the main table (it adds noise for most users), but here's what to look for on eBay:
- Search "Tesla M40 24GB"
- Filter to "Used" condition, $60-120 range
- Avoid listings that say "for parts" or "untested"
- Check seller feedback (datacenter liquidators are usually safe)
Good luck, and may your thermals be ever in your favor.
Related
- Tesla P100 Review - 16GB HBM2, faster than P40 for smaller models
- Tesla K80 Review - Even cheaper, even worse
- How we estimate inference speed