Short Answer: A single Quadro P6000 (24 GB VRAM) can run many publicly released LLMs out of the box (especially smaller to mid‐sized models, or larger models in quantized form), but not all of them in their full‐precision versions. You can still run bigger models if you use methods like multi‐GPU setups, CPU offloading, or lower‐precision (quantized) weights.
Below is a more detailed explanation.
1. VRAM Requirements for LLMs
- Model Size → VRAM Demand
- The larger the model, the more GPU memory is needed to store weights and intermediate activations during inference or training.
- For example, a 7–13 B parameter model can often fit comfortably on a 24 GB GPU. But models with 30 B, 40 B, or even 70+ B parameters can require far more VRAM unless you use optimizations (quantization, CPU offloading, etc.).
- Precision and Quantization
- Running a model in fp32 (full 32‐bit floats) demands the most memory.
- Many modern frameworks and libraries (e.g., bitsandbytes, GPTQ, AutoGPTQ, ExLlama, etc.) allow you to load models in 8‐bit or 4‐bit precision, significantly reducing VRAM requirements—often by half or more.
- With 24 GB, you can fit some of the 30 B–40 B parameter models at 4‐bit or 8‐bit precision for inference.
- Multi‐GPU and CPU Offloading
- If a single 24 GB card is not enough, frameworks like DeepSpeed, Accelerate (from HuggingFace), or TensorParallel can split a large model across multiple GPUs.
- Alternatively, part of the model can be offloaded to CPU memory. This works but can slow down inference speeds.
2. Examples of Popular/Open LLMs
- GPT-2 (up to 1.5 B parameters)
- Easily runs on 4–8 GB of VRAM. So 24 GB is plenty.
- GPT-Neo/GPT-J/GPT-NeoX (6 B–20 B parameters)
- GPT-J (6 B) fits on ~13–16 GB in fp16. Very comfortable on a 24 GB card.
- GPT-NeoX (20 B) typically requires ~38–40 GB in fp16. However, with 8‐bit or 4‐bit quantization, it becomes feasible on 24 GB.
- LLaMA Family (7–65 B parameters)
- 7 B and 13 B easily fit on a 24 GB GPU in 8/4‐bit.
- 30 B can be made to fit using 8‐bit or 4‐bit, though it might be tight depending on batch size.
- 65 B typically exceeds 24 GB alone, so you’d either need multi‐GPU or heavy CPU offloading + quantization.
- Falcon, MPT, WizardLM, etc.
- Similarly, smaller variants (<15 B parameters) will fit easily; larger variants (30–40 B) can fit with 4‐bit or 8‐bit quantization.
- GPT-3 and GPT-4
- These are not publicly released in full “host your own” form. You only have API access. So you’re not running them locally in full anyway.
3. Practical Takeaways
- Out of the Box
- Any LLM under ~13 B parameters in standard half‐precision (fp16) is basically trivial for a 24 GB Quadro P6000.
- Going above 20–30 B parameters usually needs 8‐bit or 4‐bit quantization or partial CPU offloading.
- Larger Models Are Still Possible
- 30–40 B parameter models often become practical on a single 24 GB GPU with 4‐bit or 8‐bit loading.
- 65 B and up typically require either multiple GPUs, heavy offloading (and thus slower inference speeds), or you’ll have to switch to an even more aggressively quantized approach.
- Multi‐GPU Setup
- If you install two Quadro P6000 cards (2 × 24 GB = 48 GB total), you can handle bigger models more comfortably. Frameworks can distribute the model’s layers or shards across both GPUs.
- Inference vs. Training
- Inference usually requires less memory than full training. So even large models can sometimes be served with a single GPU (especially if you reduce precision).
- Training (especially fine‐tuning) can demand much more memory overhead for gradients and optimizer states. This often requires either more GPUs or specialized methods like LoRA or QLoRA fine‐tuning, which drastically reduce VRAM usage.
Bottom Line
- Yes, the Quadro P6000 can run the great majority of openly downloadable LLMs, as long as they’re within the VRAM constraints (via quantization or CPU offload if needed).
- A single 24 GB GPU is not enough for the largest models (50+ B parameters) at full precision, but quantization or distributing across multiple GPUs can make it work.