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Evaluating Tesla M40 GPUs for Local AI

September 3, 2026

Evaluating Tesla M40 GPUs for Local AI

PUBLIC ARCHIVE EDITION

Provenance

  • Provider: ChatGPT
  • Original title: New chat
  • Conversation ID: 68c09d70-9de0-832f-9f95-d1c8a60fc1db
  • Created: 2025-09-09T14:34:46-07:00
  • Updated: 2025-09-09T14:41:31-07:00
  • Models: gpt-5, gpt-5-thinking
  • Source archive SHA-256: 0f7aee663dace8d779cc4e6f229b4e22a8386f8075b81801a3c8c2b112e1bdd7
  • Source transcript SHA-256: 80bf6aacfe34307d789eadde083705c3d0e47607cb5a46013e1da8390f5e1db5
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  • Record ID: chatgpt:68c09d70-9de0-832f-9f95-d1c8a60fc1db
  • Source member: conversations-000.json
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  • Editorial changes: privacy-approved local edits preserved; approved editorial title applied
  • Publication/version history: public archive edition v1

Conversation

Darren — 2025-09-09T14:34:45-07:00

I know we kind of gave a pass on these cards when we were first looking, but this is an awful lot of memory not to look at it. I’m going to give you this link. Let me see what you think.

Nvidia Tesla M40 24GB GPU Accelerator 900-2G600-0010-000, Model PG600 x4

ChatGPT — 2025-09-09T14:34:49-07:00 · gpt-5

(Referenced images: การ์ด NVIDIA TESLA M40 24GB SERVER ACCELERATOR GRAPHICS PROCESSING UNIT …; nVidia Tesla M40 GPU 24GB GDDR5 Accelerator Processing Card 900-2G600 …; NVIDIA TESLA M40 24GB GDDR5 PCI-E 3.0X16 GPU CARD CUDA PG600; Nvidia Tesla M40 24GB GPU GDDR5 PCI-E 3.0 x16 Accelerator Graphics …; NVIDIa® Tesla® M40 – GPU aCCeleRaTOR; NVIDIA Tesla M40 24GB GDDR5 PCI Express 3.0 GPU …; NVIDIA Tesla M40 Passive Graphic Card – 24 GB GDDR5 …; Nvidia Tesla M40 24GB GPU Accelerator 900-2G600-0010 …; Nvidia Tesla M40 24GB GPU Accelerator 900-2G600-0010 …; Nvidia Tesla M40 Accelerator Card MY6DR; Regarding NVIDIA TESLA M40 (24GB), is it the same as an …; NVIDIA Tesla M40 PG600 12GB GDDR5 GPU Graphics …)

Nice, these images show the NVIDIA Tesla M40 24 GB GPU accelerator (Model PG600, Part Number 900-2G600-0010-000) quite clearly — you’ll recognize the distinctive green-and-black shroud typical of Tesla cards.

Key Specs & Features

The Tesla M40 is a high-performance compute GPU based on NVIDIA’s Maxwell architecture. It was specifically designed for deep learning training tasks. Key specifications include:

This variant is often referred to as the PG600 model, part number 900-2G600-0010-000, and also appears under Dell’s inventory codes like MY6DR / 0MY6DR. (Sources: NVIDIA Tesla M40 24GB GDDR5 PCI Express 3.0 GPU …; Nvidia Tesla M40 24GB GPU Accelerator 900-2G600-0010 …; Nvidia Tesla M40 Accelerator Card MY6DR)

Performance & Use Cases

The Tesla M40 was a powerhouse for its time, offering up to 13× faster training times compared to CPU-only setups, particularly in frameworks like Caffe and Torch for models such as AlexNet. (Sources: NVIDIa® Tesla® M40 – GPU aCCeleRaTOR)

However, it does not have FP16 (half-precision) support, which is now standard in later high-performance GPUs for deep learning. That means it underperforms relative to newer GPUs when running modern models optimized for FP16 or better mixed-precision training/inference. (Sources: Regarding NVIDIA TESLA M40 (24GB), is it the same as an …)

Practical Considerations

  • Legacy but functional: Many users note that while the M40 still “runs code”, it’s behind newer Pascal-series GPUs like the P40 in efficiency and power draw. One rough estimate from a user claimed about half a token per second when running large models like 30B models, using four M40s. (Sources: Regarding NVIDIA TESLA M40 (24GB), is it the same as an …)

  • Compatibility quirks: Given its age and typical usage in server platforms, you might encounter setup or resource allocation issues, especially in older PCs or consumer-grade motherboards. One user even reported a “Device can’t find enough free resources (code 12)” scenario, which required debugging. (Sources: NVIDIA Tesla M40 PG600 12GB GDDR5 GPU Graphics …)

Summary Table

Feature Detail
Memory 24 GB GDDR5
Architecture Maxwell (GM200)
CUDA Cores 3072
Single-Precision FP ~7 TFLOPS (with Boost)
Power Draw ~250 W
Cooling Passive (thermal heatsink)
Compute API Support CUDA, OpenCL, DirectCompute, OpenACC
Key Use Deep learning training, compute tasks
Limitations No FP16, high power use, older PCIe era

Final Thoughts

If you have this GPU available and just want solid FP32 compute power with plenty of memory, the Tesla M40 is still a reliable card. It’s especially useful if budget is a concern and higher-end GPUs aren’t an option.

But if you’re pushing the envelope on modern models — especially those optimized for FP16 or mixed precision — newer GPUs like the Tesla P-series (P40, P100, etc.) or even the modern A-series are far more efficient and cost-effective in the long run. Let me know if you want advice comparing it to those or planning deployment in a multi-GPU setup!

Darren — 2025-09-09T14:37:08-07:00

NVIDIA GeForce GTX Titan Xp 12GB GDDR5X Graphics Card – 900-1G611-2530-000

If I remember right, this is along the same lines, but it got a better rating from you.

ChatGPT — 2025-09-09T14:37:11-07:00 · gpt-5

(Referenced images: NVIDIA GeForce GTX Titan Xp 12GB GDDR5X PCIe Graphic/Video Card GPU | Jawa; NVIDIA Star Wars TITAN Xp Collector Edition Review; NVIDIA GeForce GTX Titan Xp 12GB GDDR5X GPU …; Review: Nvidia Titan Xp – Graphics; NVidia Titan Xp Review vs. 1080 Ti: $200 Per Percentage …; NVIDIA_TITAN_Xp_User_Guide.pdf; GeForce 10 series; Nvidia Titan Xp 12GB Review; NVIDIA GeForce GTX Titan Xp 12GB GDDR5X Graphics …; Is the GTX Titan Xp good for gaming? : r/nvidia)

Here’s a look at your NVIDIA GeForce GTX Titan Xp 12 GB GDDR5X (model 900-1G611-2530-000)—a striking card with a blower-style reference cooler and the “GeForce GTX” green lettering.

Specs & Architecture

Performance & Context

How It Stacks Up Against the Tesla M40

Feature Tesla M40 (Maxwell, 24 GB) Titan Xp (Pascal, 12 GB)
CUDA Cores ~3,072 3,840
FP32 Performance ~7 TFLOPS ~12 TFLOPS
Memory Type GDDR5 (slower) GDDR5X (~548 GB/s)
FP16 Support None None
Target Use Compute/deep learning Gaming & compute, enthusiast
Power Draw & Cooling Passive, server-oriented Blower-style, desktop-aimed
Pricing (at launch) Lower but older design Much higher (flagship)
  • Compute vs. Gaming Focus: The Tesla M40 is built for compute-heavy tasks and offers huge memory but lags in raw FP32 throughput and memory speed. The Titan Xp delivers significantly more compute performance and memory bandwidth, making it better for both deep learning (FP32 workloads) and gaming.
  • Feature Set: Both lack FP16 acceleration, which is a drawback compared to newer GPUs like RTX series that support mixed precision.
  • Thermals & Compatibility: The M40 thrives in server environments with passive cooling, while the Titan Xp is better suited to desktop PCs with active airflow.

Bottom Line

Yes, you remembered right—this Titan Xp generally earns a better place in the hierarchy than the M40, especially for gaming and general compute workloads. The Titan Xp’s architecture, memory speed, bandwidth, and overall throughput give it a clear advantage in many scenarios.

If your goal is deep learning or training large models, the Titan Xp will serve better in FP32 workloads—but if you want more modern efficiency or FP16/mixed-precision benefits, something like the Tesla P100 or Nvidia’s RTX/compute-focused GPUs (e.g., A-series or RTX 6000/8000) might be more future-proof.

Let me know if you’d like a side-by-side comparison with newer cards or thoughts on multi-GPU setups!

Darren — 2025-09-09T14:38:52-07:00

Oh, I suppose I should set those parameters. This is basically me looking into what I can afford slash what is available for me to insert into little Oogway’s device carriage to replace the current 1060 with something that has at least 12 gigabyte of memory and will perform admirably at the job we want to use it for to run that 7-bit model of K2.

ChatGPT — 2025-09-09T14:39:15-07:00 · gpt-5-thinking

Got it—goal is a budget-friendly drop-in upgrade for Little Ougway that has ≥12 GB VRAM and runs a 7-bit “K2” model smoothly.

Here’s the short list, in order of bang-for-buck for LLM inference:

1) RTX 3060 12 GB (Ampere) — best cheap/modern pick

2) Tesla P40 24 GB (Pascal) — cheapest way to get big VRAM

  • Why: 24 GB gives breathing room for larger quantizations and bigger context. Great $/VRAM for local LLMs.
  • Caveats: passive server card (needs strong front-to-back airflow), no display outputs, 250 W, fits best in a well-ventilated case; sometimes resource quirks on consumer boards.
  • Street price (used): often $270–$400. (Sources: Nvidia P40; NVIDIA 900-2G610-0000-000 Tesla P40 24GB GDDR5 …)

3) RTX 3090 24 GB (Ampere) — stretch option, “it just works fast”

What about Titan Xp 12 GB and Tesla M40 24 GB?

How much VRAM do you really need for 7-bit?

Very rough rule of thumb for quantized LLMs (model params × 0.875 bytes, plus ~20–30% overhead for KV/cache/ops):

  • 7B @ 7-bit → ~6.1 GB + overhead ⇒ runs fine on 12 GB.
  • 13B @ 7-bit → ~11.4 GB + overhead ⇒ 12 GB works, but tight; 16–24 GB is comfier.
  • 20B+ @ 7-bit → you’ll want ≥16–24 GB.

If K2 is around 13B, a 12 GB card will do with careful settings; if it’s bigger or you want larger context windows, 24 GB is the sweet spot.

Quick compatibility checklist for your rig (Biostar BTC-250 + i7 box)

  • PSU:
  • RTX 3060: ~550–600 W PSU, 1×8-pin.
  • P40/M40: 250 W each; ensure a solid 600–700 W PSU and good case airflow (server cards want strong front-to-back cooling).
  • RTX 3090: 750–850 W PSU, 2×8-pin.
  • Drivers / Linux: Stick to recent NVIDIA drivers on Ubuntu; Ampere (30-series) has the healthiest support for modern inference libs.
  • Physical fit: All are dual-slot, full-length; Tesla cards are passive—plan airflow (high-CFM intake + ducting if possible).

My recommendation for Little Ougway today

  • On a tight budget: RTX 3060 12 GB (best balance of cost, ease, and modern kernels).
  • If you need headroom for bigger K2 variants / context: Tesla P40 24 GB (accept the airflow/no-display quirks) or, if funds allow, RTX 3090 24 GB for maximum hassle-free speed.

If you toss me the exact K2 parameter count you’re targeting and a rough budget ceiling, I’ll pin this down to a single pick and give you BIOS/driver settings to flip before the swap.

Darren — 2025-09-09T14:41:14-07:00

ASUS NVIDIA GeForce RTX 3060 12GB GDDR6 Graphic Card ( DUAL-RTX3060-O12G-V2)

This one looks like a gamer kid that, um, actually takes care of his stuff. It says it’s open box, but it is used. Um… I know this is right up our alley.

ChatGPT — 2025-09-09T14:41:14-07:00 · gpt-5

That one — the ASUS Dual RTX 3060 12 GB (V2) — is right in the bullseye for what you said you need. Let’s break it down in plain terms:

✅ Why It Fits Little Ougway’s Needs

  • VRAM: 12 GB GDDR6. Enough headroom for a 7-bit K2 model (7B runs comfortably, 13B can fit with some tuning).
  • Architecture: Ampere → has Tensor Cores and modern CUDA support. That means it plays nicely with today’s inference frameworks (PyTorch, CUDA 12.x, transformers, etc.).
  • Efficiency: TDP around 170 W → runs off a single 8-pin. Won’t stress your PSU like a 3090 would.
  • Compatibility: Consumer-grade card with HDMI/DP outputs and active cooling. No server airflow hacks needed (unlike Tesla cards).

⚖️ Open Box / Used Considerations

  • Open box usually means returned stock, not mined-to-death.
  • Check warranty: ASUS often ties warranty to the serial, not the buyer, so you might still have coverage.
  • Inspect fans: If the seller says “used but well cared for,” look at pictures — clean shroud, no bent fins, no dust buildup = good sign.
  • Thermal paste: If it’s lightly used, no worries. If heavily used, you may want to repaste/repad down the line (easy DIY if needed).

⚡ Performance Expectation

  • Benchmarks put the RTX 3060 12 GB at about 13 TFLOPS FP32, almost double the Titan Xp and nearly twice as efficient as a Tesla M40.
  • In practice, you’ll see smoother inference speed for 7-bit models compared to any of the older 12–24 GB cards, because the Ampere Tensor Cores accelerate quantized math.
  • It’s basically the sweet spot for budget AI tinkering: enough VRAM to handle 7–13B quantizations, without being power-hungry or quirky.

🛠️ My Take

Yes — this is right up your alley. If it’s in good shape and priced fair (I’d call $220–$280 a very good deal in today’s used market), this will make Little Ougway way happier than the 1060, and you won’t have to wrestle with server-card oddities.

Do you want me to map out exact VRAM footprints for 7B, 13B, and maybe 20B at 7-bit so you’ll know exactly how far this 3060 can stretch before you’d need to jump up to a 24 GB card?