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AI hardware comparison
Compare 15 GPUs and AI systems for running large language models: data center GPUs from NVIDIA and AMD, workstation cards and desktop AI systems. See memory, bandwidth, compute and power side by side, and which open-weight models each one can run. We don’t sell hardware, so there are no buy links.
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Popular comparisons
The comparisons people search for most, each with a verdict, full specs and which models fit.
Data center GPUs
Workstation and PCIe GPUs
Desktop AI systems
All hardware at a glance
One GPU or system each. The last column is the single-user token speed ceiling for Llama 3.3 70B at 4-bit; “Needs 2+” means it does not fit on one. For your own model and team size, use the VRAM calculator.
| Hardware | Memory | Bandwidth | FP8 dense | Power | Llama 70B 4-bit ceiling |
|---|---|---|---|---|---|
| NVIDIA A100 80GB Data center GPU | 80 GB | 2.04 TB/s | Not supported | 400 W | 53 tok/s |
| NVIDIA H100 Data center GPU | 80 GB | 3.35 TB/s | 1,979 TFLOPS | Up to 700 W | 86 tok/s |
| NVIDIA H200 Data center GPU | 141 GB | 4.8 TB/s | 1,979 TFLOPS | Up to 700 W | 124 tok/s |
| NVIDIA B200 Data center GPU | 180 GB | 8 TB/s | 4,500 TFLOPS | Up to 1,000 W | 206 tok/s |
| NVIDIA B300 Data center GPU | 288 GB | 8 TB/s | 4,500 TFLOPS | About 1,100 W | 206 tok/s |
| AMD Instinct MI300X Data center GPU | 192 GB | 5.3 TB/s | 2,615 TFLOPS | 750 W | 136 tok/s |
| AMD Instinct MI325X Data center GPU | 256 GB | 6 TB/s | 2,615 TFLOPS | 1,000 W | 155 tok/s |
| NVIDIA L40S Data center PCIe GPU | 48 GB | 864 GB/s | 733 TFLOPS | 350 W | Needs 2+ |
| NVIDIA RTX PRO 6000 Blackwell Workstation and server GPU | 96 GB | 1.79 TB/s | Not published | Up to 600 W | 46 tok/s |
| NVIDIA GeForce RTX 5090 Consumer GPU | 32 GB | 1.79 TB/s | Not published | 575 W | Needs 2+ |
| NVIDIA DGX Spark Desktop AI system | 128 GB | 273 GB/s | Not published | 240 W power supply | 7 tok/s |
| ASUS Ascent GX10 Desktop AI system | 128 GB | 273 GB/s | Not published | 240 W power supply | 7 tok/s |
| NVIDIA Jetson AGX Thor Robotics and edge module | 128 GB | 273 GB/s | Not published | 40 to 130 W | 7 tok/s |
| AMD Ryzen AI Max+ 395 (Strix Halo) Mini PC chip | 128 GB | 256 GB/s | Not published | 45 to 120 W | 7 tok/s |
| Apple Mac Studio M3 Ultra Desktop computer | 512 GB max | 819 GB/s | Not published | Up to 480 W | 21 tok/s |
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
AI hardware questions
Which GPU is best for running LLMs?
It depends on model size and users. For a few users on models up to about 30B parameters, a 32 GB RTX 5090 or a 96 GB RTX PRO 6000 is enough. For 70B-class models serving a team, H100, H200 or MI300X servers are the usual choice. For the largest open-weight models, such as DeepSeek or Kimi, plan for 8-GPU H200, B200, B300 or MI325X servers.
Is memory or bandwidth more important for LLMs?
Both, for different reasons. Memory decides whether a model fits and how many users’ context you can hold. Bandwidth decides how fast tokens are generated, because every generated token reads the model’s weights from memory. A large-memory, low-bandwidth system such as DGX Spark can hold big models but answers slowly.
How much GPU memory do I need for a 70B model?
Llama 3.3 70B needs about 46 GB at 4-bit for one user with 8K context, about 80 GB at FP8, and more for each extra user. Use the VRAM calculator for your exact model, precision and team size.
Are AMD GPUs good for running LLMs?
Yes. AMD Instinct MI300X and MI325X have more memory per GPU than NVIDIA’s H100 and H200, and popular serving engines such as vLLM and SGLang support AMD’s ROCm software. Some NVIDIA-only tools, such as TensorRT-LLM, do not run on AMD.
What is the best desktop for running local LLMs?
DGX Spark, ASUS Ascent GX10 and AMD Ryzen AI Max+ 395 mini PCs all offer 128 GB of unified memory for models up to roughly 130B to 170B parameters at 4-bit, with similar token speeds. Mac Studio M3 Ultra offers up to 512 GB and about 3× their bandwidth. An RTX 5090 desktop is the fastest for models that fit in 32 GB.
How are these comparisons made?
We use manufacturer specifications for memory, bandwidth, compute and power, and our VRAM calculator for which models fit. Speed ceilings are memory bandwidth divided by model size for a single user; real speeds are usually 50 to 80% of that. We do not sell hardware and earn nothing from these comparisons.
Specifications from the manufacturer pages: NVIDIA A100, NVIDIA H100, NVIDIA H200, NVIDIA DGX B200, NVIDIA DGX B300, AMD Instinct MI300X, AMD Instinct MI325X, NVIDIA L40S, NVIDIA RTX PRO 6000 Blackwell, NVIDIA GeForce RTX 5090, NVIDIA DGX Spark, ASUS Ascent GX10, NVIDIA Jetson Thor, AMD Ryzen AI Max+ 395, Apple Mac Studio (2025). Last checked November 10, 2026.
Not sure which hardware fits your team?
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, and helps you size the hardware first.