Comparison_of_Nvidia_GPUs_for_Fine-Tuning.md

Comparison of Nvidia GPUs for Fine-Tuning

Key Considerations for Fine-Tuning:

1. VRAM Capacity: Determines the maximum size of the model and the batch size you can use. Running out of VRAM is a common bottleneck.
2. Memory Bandwidth: How quickly data (model parameters, activations, gradients) can be moved between the GPU's memory and its compute units. High bandwidth is essential for keeping the powerful cores fed, especially with large models.
3. Compute Performance (FLOPS/TOPS): Raw processing power. Modern fine-tuning heavily relies on Tensor Cores for mixed-precision training (FP16, BF16, TF32, and increasingly FP8 on newer architectures like Hopper, Ada Lovelace, and Blackwell).
4. Architecture: Newer architectures (Blackwell > Hopper > Ada Lovelace) generally offer better performance per watt, more efficient Tensor Cores, and support for newer data formats (like FP8).
5. Target Environment: Data Center cards (B200, H200, H100, L40S, L4) are built for 24/7 operation, often have passive cooling (requiring server airflow), and support features like NVLink for high-speed multi-GPU communication. Workstation cards (RTX Ada series) are for professional desktops, have active cooling, ECC memory (except RTX 4090), and professional drivers. Consumer cards (RTX 4090) offer high performance for their price but lack ECC, have different drivers, and aren't designed for continuous data center loads.

Here is a comparison table. Note that B200 specs are based on initial announcements and may be refined. Performance figures (like TOPS) can vary depending on sparsity and specific operation type; these are representative peak values. I'll primarily use FP16/BF16 or FP8 where available as relevant mixed-precision metrics. I'll assume the L40 mentioned is the compute-focused L40S, which is more relevant for training than the original L40.

GPU Comparison for Fine-Tuning

Feature	B200 (Single GPU)*	H200 (SXM5)	H100 (SXM5)	L40S	RTX 6000 Ada	RTX 4090	L4	RTX 4000 Ada	RTX 2000 Ada
Architecture	Blackwell	Hopper	Hopper	Ada Lovelace	Ada Lovelace	Ada Lovelace	Ada Lovelace	Ada Lovelace	Ada Lovelace
Target Use	Data Center (AI/HPC)	Data Center (AI/HPC)	Data Center (AI/HPC)	Data Center (AI/Vis)	Workstation (AI/Vis)	Consumer (Gaming/AI)	Data Center (Inf/AI)	Workstation (AI/Vis)	Workstation (Vis/AI)
VRAM Size	192 GB	141 GB	80 GB	48 GB	48 GB	24 GB	24 GB	20 GB	16 GB
VRAM Type	HBM3e	HBM3e	HBM3	GDDR6 ECC	GDDR6 ECC	GDDR6X	GDDR6	GDDR6 ECC	GDDR6 ECC
Memory Bandwidth	~8 TB/s	~4.8 TB/s	~3.35 TB/s	~864 GB/s	~960 GB/s	~1008 GB/s	~504 GB/s	~400 GB/s	~288 GB/s
FP8 Tensor TOPS	~18,000 (Est.)	~3958	~3958	~1451	~1453	~1321	~484	~380	~226
FP16/BF16 Tensor	~9,000 (Est.)	~1979	~1979	~725	~726	~660	~242	~190	~113
TF32 Tensor	~4,500 (Est.)	~989	~989	~363	~363	~330	~121	~95	~56
CUDA Cores	~TBD (Very High)	16896	16896	18176	18176	16384	7424	6144	2816
NVLink/NVSwitch	Yes (High Speed)	Yes (900 GB/s)	Yes (900 GB/s)	No (PCIe Gen4)	No (PCIe Gen4)	No (PCIe Gen4)	No (PCIe Gen4)	No (PCIe Gen4)	No (PCIe Gen4)
TDP	~1000 W (Est.)	~700 W - 1000 W	~700 W	~350 W	~300 W	~450 W	~72 W	~130 W	~70 W

• Note on B200: Specs are preliminary and often discussed in the context of the GB200 Superchip (2x B200 + Grace CPU). Single B200 performance/TDP estimates are derived. Peak FLOPS can reach higher with sparsity.
• Note on H100/H200: SXM variants listed, which have higher bandwidth and TDP than PCIe versions. PCIe H100 has 80GB HBM2e (~2 TB/s bandwidth). PCIe H200 has 141GB HBM3e (~4.1 TB/s bandwidth).
• Note on L40 vs L40S: Table assumes L40S (more compute-focused). Original L40 has similar memory but lower compute (~90 TFLOPS FP16).
• Note on RTX 4090: Highest raw performance/dollar outside of data center cards, but consumer focus (drivers, cooling, no ECC, limited multi-GPU).
• Note on TOPS: Represents peak theoretical throughput with sparsity for FP8 and without for FP16/BF16/TF32 where applicable. Real-world performance depends heavily on the specific workload, software stack, and utilization.

Performance Hierarchy for Fine-Tuning (Rough):

1. B200: Absolute cutting edge. Massive VRAM and bandwidth, highest compute. For state-of-the-art, largest models. Likely very expensive and power-hungry.
2. H200: Significant upgrade over H100, primarily due to much larger/faster VRAM. Excellent for models bottlenecked by H100's VRAM.
3. H100: Previous generation flagship, still extremely powerful. Gold standard for large model training/fine-tuning before B200/H200. FP8 support is key.
4. L40S / RTX 6000 Ada: Roughly comparable compute performance. L40S is data center focused (passive cooling option, better for servers), RTX 6000 Ada is workstation focused (active cooling, ECC). Both offer large 48GB VRAM, making them very capable for fine-tuning fairly large models that don't fit on the 24GB cards. High Ada Lovelace compute.
5. RTX 4090: Highest compute performance in the consumer/prosumer space, beating L40S/6000 Ada slightly in some raw metrics due to GDDR6X bandwidth. However, limited to 24GB VRAM, no ECC, and consumer focus. Excellent value if 24GB is sufficient and operating environment is suitable.
6. L4: Data center card focused on inference and efficiency. Has decent 24GB VRAM and Ada compute, but much lower bandwidth and compute than 4090/L40S. Its low power (72W) is a major advantage in dense deployments or power-constrained environments. Fine-tuning smaller models is feasible.
7. RTX 4000 Ada: Solid mid-range workstation card. 20GB VRAM is useful. Performance is a noticeable step down from the higher-tier Ada cards but significantly better than the RTX 2000 Ada. Good for fine-tuning moderately sized models.
8. RTX 2000 Ada: Entry-level professional Ada card. 16GB VRAM is adequate for smaller models or tasks where VRAM isn't the primary constraint. Compute power is significantly lower than other options here. Best suited for less demanding fine-tuning tasks or where budget/power (70W) is very limited.

Which to Choose?

• Budget no object, largest models: B200 or H200 (availability/cost may dictate).
• High-end data center, large models: H100 remains a strong choice.
• Need > 24GB VRAM, but H100/H200 out of reach: L40S (server) or RTX 6000 Ada (workstation).
• Best performance under ~$2k (and 24GB VRAM is enough): RTX 4090 (if consumer card limitations are acceptable).
• Need 24GB VRAM in a power-efficient data center card: L4 (but expect lower performance than 4090).
• Mid-range workstation fine-tuning: RTX 4000 Ada offers a good balance of VRAM (20GB) and performance for its segment.
• Entry-level/Power-constrained workstation: RTX 2000 Ada (if 16GB is sufficient and performance needs are modest).

Consider the size of the models you plan to fine-tune and your budget/infrastructure constraints when making your final decision. VRAM is often the first bottleneck you'll hit.