AI Chips
NVIDIA H100, H200, B200, B300 and Rubin, plus AMD Instinct and Google TPU, compared on compute, memory and what they are built for.
AI chips compared: NVIDIA, AMD, Google, AWS, Intel and Groq
Head-to-head comparisons and explainers for the chips that train and run AI, from the NVIDIA H100 to Rubin, AMD Instinct, Google TPUs, AWS Trainium, Intel Gaudi and Groq LPUs. Every specification is given per chip and without sparsity, and links to the vendor's own documentation.
Learn to read a spec sheet first, then compare generations and vendors, or open the specs database to compare 28 chips side by side.
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Edited by Sami Ullah Khan. Read our editorial policy.
All AI Chips articles
NVIDIA Rubin Explained: Vera Rubin NVL72 Specs, HBM4 and Rubin vs Blackwell
NVIDIA Rubin is the GPU after Blackwell: 288 GB of HBM4 at 19.2 TB/s, 35 PFLOPS of dense NVFP4 and NVLink 6, paired with…
GB200 NVL72 Explained: 72 GPUs, One NVLink Domain
GB200 NVL72 puts 72 Blackwell GPUs and 36 Grace CPUs in one liquid-cooled rack with 130 TB/s NVLink and 13.4 TB HBM3e. Specs, what…
B200 vs H100: Blackwell vs Hopper Specs and Speed
B200 vs H100: 2.3x the FP16 and FP8 Tensor Core throughput, FP4 at 9 PFLOPS, 180 GB at 8 TB/s, 1.8 TB/s NVLink and…
H100 vs H200: Same GPU, More Memory (Specs Compared)
H200 uses the same Hopper GPU as H100 with 141 GB of HBM3e at 4.8 TB/s instead of 80 GB at 3.35 TB/s. What…
H100 vs A100: Specs, Speed and Which to Choose
H100 vs A100 compared: 989 vs 312 dense FP16 TFLOPS, FP8, 3.35 vs 2.04 TB/s memory bandwidth, NVLink, power and real-world speedups.
NVIDIA DGX Spark Explained: Specs, LLM Speed and Who It Is For
DGX Spark packs a GB10 Grace Blackwell chip and 128 GB of unified memory into a mini PC. Specs, which models fit, real LLM…
TPU vs GPU: What Is the Difference for AI? (2026)
TPUs are Google's systolic-array AI chips; GPUs are general-purpose with Tensor Cores. Architecture, Ironwood vs B200 specs, scaling, software and which to choose.
LLM Quantization Explained: GGUF, GPTQ, AWQ, FP8 and FP4
How quantization shrinks models to 4 bits, what GGUF Q4_K_M, GPTQ, AWQ, NF4, FP8 and FP4 mean, which to use, and how much quality…
Best GPU for Running LLMs Locally (2026 Guide)
VRAM decides which models you can run and bandwidth decides speed. GPUs and Macs compared for Llama, Qwen, DeepSeek and gpt-oss, with VRAM needs…
How to Read a GPU Spec Sheet: TFLOPS, Sparsity and Memory
Dense vs sparse TFLOPS, per-GPU vs per-server figures, SXM vs PCIe, memory bandwidth, the peak FLOPS formula and how to estimate real training time…