EDGE OSAI HARDWARE COMPARISON
B200 vs B300Specs, memory and LLM performance
The B300 (Blackwell Ultra) raises memory from 180 GB to 288 GB of HBM3e per GPU and dense FP4 compute by about 50%, with the same 8 TB/s bandwidth and higher power.
Memory
180 GB vs 288 GB
Bandwidth
8 TB/s vs 8 TB/s
Power
Up to 1,000 W vs About 1,100 W
Type
Data center GPU vs Data center GPU
Which should you choose?
CHOOSE B200 IF
Your models fit in 180 GB per GPU and you want earlier availability and lower power.
CHOOSE B300 IF
You serve trillion-parameter models or long-context reasoning, where 288 GB per GPU cuts the number of GPUs you need.
Still deciding between B200 and B300?
Tell us your models, users and data. We will recommend a setup and run it inside your own building with Vanik Edge OS. Free, no obligation.
B200 vs B300 specs
Manufacturer specifications. Green marks the stronger figure where a higher number is better. Dense compute is shown without sparsity.
| Specification | NVIDIA B200 | NVIDIA B300 |
|---|---|---|
| Type | Data center GPU | Data center GPU |
| Memory | 180 GB | 288 GB |
| Memory type | HBM3e | HBM3e |
| Memory bandwidth | 8 TB/s | 8 TB/s |
| FP16 / BF16 (dense) | 2,250 TFLOPS | 2,250 TFLOPS |
| FP8 (dense) | 4,500 TFLOPS | 4,500 TFLOPS |
| FP4 (dense) | 9,000 TFLOPS | 13,500 TFLOPS |
| Power | Up to 1,000 W | About 1,100 W |
| Multi-GPU link | NVLink 5: 1.8 TB/s | NVLink 5: 1.8 TB/s |
| Form factor | Module in 8-GPU HGX B200 and DGX B200 servers | Module in 8-GPU HGX B300 and DGX B300 servers |
| Architecture | Blackwell, 2024 | Blackwell Ultra, 2025 |
| Software | CUDA, TensorRT-LLM, vLLM, SGLang | CUDA, TensorRT-LLM, vLLM, SGLang |
| Price | No list price: sold in servers through OEMs and cloud providers | No list price: sold in servers through OEMs and cloud providers |
What they mean for running LLMs
For large language models, memory decides which models fit and how many users you can serve, and memory bandwidth decides how fast each user gets tokens. These are planning figures for one GPU or system using a 4-bit model.
| For LLMs | B200 | B300 |
|---|---|---|
| Usable for models | About 162 GB | About 259 GB |
| Largest dense model at 4-bit (1 user, short context) | About 260B parameters | About 420B parameters |
| Llama 3.3 70B at 4-bit, single-user speed ceiling | About 206 tokens/s | About 206 tokens/s |
Which open-weight models fit
Memory needed for one user with 8K context at the lowest precision available for each model. Open a model for team-size and fine-tuning figures.
| Model (1 user, 8K context, lowest available precision) | B200 | B300 |
|---|---|---|
| gpt-oss-20b 21B · about 15 GB at MXFP4 | Yes | Yes |
| Gemma 4 26B A4B 25.2B · about 16 GB at 4-bit | Yes | Yes |
| Qwen3.6-27B 27.78B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.8-27B 27.8B · about 24 GB at NVFP4 | Yes | Yes |
| Gemma 4 31B 30.7B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.6-35B-A3B 35B · about 24 GB at NVFP4 | Yes | Yes |
| Llama 3.3 70B Instruct 70.6B · about 46 GB at INT4 | Yes | Yes |
| gpt-oss-120b 117B · about 70 GB at MXFP4 | Yes | Yes |
| MiniMax-M2.5 229B · about 135 GB at 4-bit | Yes | Yes |
| GLM-5.3-Flash 320B · about 192 GB at NVFP4 | 2 GPUs | Yes |
| DeepSeek-V4.1-Flash 552B · about 320 GB at 4-bit | 2 GPUs | 2 GPUs |
| DeepSeek-R1 671B · about 410 GB at 4-bit | 4 GPUs | 2 GPUs |
| DeepSeek-V3.2 685B · about 419 GB at NVFP4 | 4 GPUs | 2 GPUs |
| GLM-5.2 744B · about 455 GB at NVFP4 | 4 GPUs | 2 GPUs |
| GLM-5.3 753B · about 460 GB at NVFP4 | 4 GPUs | 2 GPUs |
| GLM-5.1 754B · about 461 GB at NVFP4 | 4 GPUs | 2 GPUs |
| Kimi K2 1T · about 579 GB at 4-bit | 4 GPUs | 4 GPUs |
| DeepSeek-V4-Pro 1.6T · about 976 GB at FP4 + FP8 | 8 GPUs | 4 GPUs |
| Kimi K3 2.8T · about 1.62 TB at MXFP4 | No: more than 8 GPUs | 8 GPUs |
Models in amber need several of these GPUs working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
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.
B200 vs B300 questions
What is the difference between the B200 and the B300?
The B300 (Blackwell Ultra) raises memory from 180 GB to 288 GB of HBM3e per GPU and dense FP4 compute by about 50%, with the same 8 TB/s bandwidth and higher power.
B200 vs B300: which is better for LLM inference?
Both have the same 8 TB/s of memory bandwidth, so they generate tokens at similar speeds when a model fits on both. The B300 holds larger models and more users’ context with 288 GB of memory versus 180 GB. Choose the B200 if your models fit in 180 GB per GPU and you want earlier availability and lower power. Choose the B300 if you serve trillion-parameter models or long-context reasoning, where 288 GB per GPU cuts the number of GPUs you need.
Can the B200 and B300 run Llama 3.3 70B?
Llama 3.3 70B needs about 46 GB of memory at 4-bit for one user with 8K context. The B200 runs it on one GPU at about 206 tokens/s at most for one user, and the B300 runs it on one GPU at about 206 tokens/s at most for one user. Speeds are theoretical ceilings from memory bandwidth; real speeds are usually 50 to 80% of that.
How much does the B200 cost compared with the B300?
B200: No list price: sold in servers through OEMs and cloud providers. B300: No list price: sold in servers through OEMs and cloud providers. Data center GPUs are usually bought as part of 4- or 8-GPU servers, so compare full-system quotes.
How are these numbers calculated?
Memory, bandwidth, compute and power are manufacturer specifications. Model memory needs come from our VRAM calculator: weights plus KV cache plus about 5 to 10% runtime overhead, keeping 10% of each GPU free (15% on GB10 systems, 25% on Mac). The speed ceiling is memory bandwidth divided by the model’s weight size. All figures are planning estimates.
Related comparisons
Specifications from the manufacturer pages for the NVIDIA DGX B200 and NVIDIA DGX B300. Speed ceilings are bandwidth divided by model size, before batching; real throughput depends on the inference engine and settings. 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.