March
How to Choose the Right GPU
A practical guide to matching your workload with the right hardware
Published: March 25, 2025
Author: Ben Moore
Read Time: 8 Minutes
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Author: Ben Moore
Read Time: 8 Minutes
How to Choose the Right GPU for Your Use Case
Even with Jensen Huang’s GTC keynotes making H100s sound like holy relics, there’s no such thing as “the best GPU”—just the best one for what you’re trying to do. And while technical forums might send you down rabbit holes of VRAM debates and spec sheets, we’ve kept this guide simple.Use Case #1: Fine-Tuning a Pretrained Model
Adapting a base model to your domain or dataset? Fine-tuning strikes the balance between full training and quick prototyping. Right-size your GPU: You don’t need a full cluster, but don’t undershoot. A100s, 4090s, or multi-GPU 3090 setups are ideal for mid-to-large runs.Best Overall (High-End, Large Model Fine-Tuning)
| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| AMD MI300X | 192GB | HBM3 | Strong FP16/BF16 performance, ROCm-compatible | Open models, PyTorch/ROCm workflows |
| NVIDIA H200 | 141GB | HBM3e | Huge capacity, fastest memory | Training huge context models, long-sequence tasks |
| NVIDIA H100 | 80GB | HBM3 | 3–4x faster than A100 on FP16/BF16 | LLMs, massive image models |
| NVIDIA A100 | 80GB | HBM2e | Mature stack, still widely used | LLMs (up to 65B with multi-GPU), large vision models |
VRAM sweet spot: Aim for 24GB+ if you’re working with large token windows or multi-modal inputs.
Best Mid-Tier (Fine-Tuning 7B–13B Models or Smaller)
| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| NVIDIA L40 / L40S | 48GB | GDDR6 | Mid-range, enterprise-ready | Fine-tuning and inference |
| NVIDIA RTX 6000 Ada | 48GB | GDDR6 ECC | Excellent FP8/BF16 support | 7B LLMs, SDXL, multimodal |
| NVIDIA A6000 | 48GB | GDDR6 | Similar to 6000 Ada workloads | LLMs, vision models |
| NVIDIA 4090 / 3090 | 24GB | GDDR6X | Strong community support, cost-effective | LoRA, QLoRA, SD pipelines |
Time vs Cost Tradeoff: Fine-tuning is bursty, so look for short-term high-performance rentals to avoid overpaying for idle time.
Best Budget Options (LoRA, QLoRA, Lightweight Fine-Tuning)
| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| NVIDIA RTX 4090 | 24GB | GDDR6X | Top-tier consumer card | Local dev, LoRA/QLoRA, SD v1.x |
| NVIDIA RTX 3090 / 3090 Ti | 24GB | GDDR6X | Aging but solid | Smaller fine-tuning workloads |
| NVIDIA RTX 4080 / 4070 Ti | 16GB | GDDR6X | Newer consumer options | Distillation, small model tuning |
LoRA, QLoRA, and PEFT methods make fine-tuning more GPU-efficient. Combine with 8-bit or 4-bit precision for even faster iteration.
Use Case #2: Production Inference
Running an API or scaling a production app? Prioritize reliability, low latency, and cost-efficiency.| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| NVIDIA H200 | 141GB | HBM3e | Max context + high throughput | Memory-intensive inference |
| NVIDIA H100 | 80GB | HBM3 | Exceptional inference throughput (FP8/BF16) | High-scale LLM and multimodal inference |
| NVIDIA A100 | 80GB | HBM2e | Excellent perf/$ for inference | Mature stack, multi-model support |
| NVIDIA L40S | 48GB | GDDR6 | Optimized for inference and graphics | Real-time inference, computer vision |
| NVIDIA RTX 6000 Ada | 48GB | GDDR6 ECC | Enterprise-grade, quiet thermals | Edge or smaller production |
| NVIDIA T4 | 16GB | GDDR6 | Low-power, efficient | Token-based inference APIs, speech, vision |
Look for verified sellers with uptime SLAs and autoscaling support.
Use Case #3: LLM Training
Training a foundation model or large fine-tuned LLM? Go big and scale smart.| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| NVIDIA H200 | 141GB | HBM3e | Huge memory capacity | Long-context and multi-modal training |
| NVIDIA H100 | 80GB | HBM3 | FP8/BF16 acceleration, NVLink support | Training 7B–70B+ models |
| NVIDIA A100 (80GB) | 80GB | HBM2e | Mature CUDA stack, multi-node | Training 13B–65B models |
| NVIDIA RTX 6000 Ada | 48GB | GDDR6 ECC | Stable performance | Single-node training |
| NVIDIA A100 (40GB) | 40GB | HBM2e | Same compute as 80GB, less VRAM | Budget multi-GPU training |
Prioritize GPUs with high VRAM (40GB+) and fast memory for token throughput. NVLink or fast interconnects boost multi-GPU performance.
Use Case #4: Image & Video Generation
Working with models like Stable Diffusion, Deforum, or open-source Sora tools?| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| NVIDIA RTX 5090 | 32GB | GDDR7 (expected) | Next-gen CUDA and memory speeds | High-res diffusion, video gen |
| NVIDIA RTX 4090 | 24GB | GDDR6X | Massive CUDA core count | High-speed image and video generation |
| NVIDIA RTX 6000 Ada | 48GB | GDDR6 ECC | Studio-grade hardware | Long-form generation, consistent throughput |
| NVIDIA A6000 | 48GB | GDDR6 | Solid FP16 performance | Batch rendering and animation |
| NVIDIA L40 / L40S | 48GB | GDDR6 | Enterprise stability | High-throughput generation |
| NVIDIA RTX 3090 / 3090 Ti | 24GB | GDDR6X | Popular with the community | Local SD pipelines, LoRA training |
Aim for 24GB+ VRAM and stable CUDA support for smooth, high-resolution workflows.
Use Case #5: Research, Education, Prototyping
Doing lightweight experimentation, demos, or model testing?| GPU | VRAM | Memory Type | Notable Features | Best For |
|---|---|---|---|---|
| NVIDIA A10 / A40 | 24GB | GDDR6 | Flexible form factors | Classroom-scale training, labs |
| NVIDIA RTX 4070 / 4070 Ti | 12–16GB | GDDR6 | Modern, cost-effective | Small batch testing, notebook dev |
| NVIDIA RTX 3080 / 3080 Ti | 10–12GB | GDDR6X | Fast CUDA cores | Student projects, image gen |
| NVIDIA V100 | 16–32GB | HBM2 | Strong FP32/FP16 support | Academic research, model testing |
| NVIDIA T4 | 16GB | GDDR6 | Low power, widely available | Education, inference demos |
Look for GPUs available on hourly or short-term rental terms. Flexibility matters more than top-end power.
Other Considerations
| Factor | Why It Matters |
|---|---|
| VRAM | Larger models and images require more VRAM |
| CUDA version | Impacts framework and driver compatibility |
| Memory bandwidth | Affects training and inference throughput |
| Price/hour | Know your tradeoff between budget and speed |