Best AI Models for NVIDIA B200 (192.0GB)
Datacenter accelerator — sold in HGX/NVL systems and typically rented in the cloud, not installed in a desktop. Listed for comparison.
With 192 GB of memory, this is a high-end configuration for local AI. You can comfortably run most open-source LLMs including large 70B parameter models at good quantization levels, making it one of the best setups for serious local AI work.
At this memory tier, nearly every popular open-source model is within reach. You can run Llama 3 70B at Q4_K_M or even Q5_K_M quantization with room to spare, handle coding assistants like DeepSeek Coder 33B at high quality, and easily run any 7B–30B model at full or near-full precision. Context windows remain generous even with larger models, so multi-turn conversations and long-document processing work smoothly.
Runs Well
- 70B models (Llama 3 70B, Qwen 72B) at Q4–Q5
- 30B models at Q6–Q8 quality
- 7B–14B models at full FP16 precision
- Vision models (LLaVA, CogVLM) without compromise
Challenging
- Mixture-of-experts models like Mixtral 8x22B at higher quants
- 120B+ models still require lower quantizations
What LLMs Can NVIDIA B200 Run?
131 models · 7 excellent
Showing compatibility for NVIDIA B200
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·120.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 43.3 GB | 120.1 t/s | 131K | EASY RUN | C38 |
Q4_K_M·277.8 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 18.7 GB | 277.8 t/s | 262K | EASY RUN | C30 |
Q4_K_M·1042.1 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 5.0 GB | 1042.1 t/s | 33K | EASY RUN | D27 |
Q4_K_M·942.0 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 5.5 GB | 942.0 t/s | 41K | EASY RUN | D27 |
Q4_K_M·287.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 18.1 GB | 287.1 t/s | 131K | EASY RUN | C30 |
Q4_K_M·981.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 981.1 t/s | 131K | EASY RUN | D27 |
Q4_K_M·1793.1 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 1793.1 t/s | 41K | EASY RUN | D26 |
Q4_K_M·253.7 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 20.5 GB | 253.7 t/s | 131K | EASY RUN | C31 |
Q4_K_M·277.8 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 18.7 GB | 277.8 t/s | 262K | EASY RUN | C30 |
Q4_K_M·977.4 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 977.4 t/s | 131K | EASY RUN | D27 |
Q4_K_M·202.4 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 25.7 GB | 202.4 t/s | 131K | EASY RUN | C32 |
Q4_K_M·116.6 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 44.6 GB | 116.6 t/s | 131K | EASY RUN | C38 |
Q4_K_M·181.9 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 28.6 GB | 181.9 t/s | 33K | EASY RUN | C33 |
Q4_K_M·1056.9 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 1056.9 t/s | 33K | EASY RUN | D27 |
Q4_K_M·646.8 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 8.0 GB | 646.8 t/s | 33K | EASY RUN | D27 |
Q4_K_M·120.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 43.3 GB | 120.1 t/s | 131K | EASY RUN | C38 |
NVIDIA B200 Specifications
- Brand
- NVIDIA
- Architecture
- Blackwell
- Compute Capability
- 10.0 (CUDA SM version)
- VRAM
- 192.0 GB HBM3e
- Memory Bandwidth
- 8000.0 GB/s
- FP16 Performance
- 2250.00 TFLOPS
- TDP
- 1000W
- Release Date
- 2024-03-18
Get Started
GPUs to Consider Over NVIDIA B200
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
Frequently Asked Questions
- Can NVIDIA B200 run Qwen3 235B A22B?
Yes, the NVIDIA B200 with 192 GB can run Qwen3 235B A22B, Qwen3 235B A22B Instruct 2507, MiniMax M2, and 1436 other models. 17 models run at excellent quality, and 16 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA B200 good for AI?
The NVIDIA B200 has 192 GB of HBM3e, making it excellent for running local AI models. It supports 33 models at good quality or better. With 8000.0 GB/s memory bandwidth, it delivers fast token generation speeds. This is an enthusiast-grade GPU that handles most popular open-source LLMs.
- How many parameters can NVIDIA B200 handle?
With 192 GB, the NVIDIA B200 supports models from 3B to 70B+ parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 320B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on NVIDIA B200?
For the best balance of quality and speed on the NVIDIA B200, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. With 24+ GB, you have the headroom to run 7B models at Q5_K_M or even Q6_K for noticeably better output quality. For larger 30B models, Q4_K_M remains the sweet spot.
- How fast is NVIDIA B200 for AI inference?
With 8000.0 GB/s memory bandwidth, the NVIDIA B200 achieves approximately 1156 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~578 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (8000 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA B200
~37 tok/s~37 tok/s~38 tok/s~37 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA B200?
The top-rated models for the NVIDIA B200 are Qwen3 235B A22B, Qwen3 235B A22B Instruct 2507, MiniMax M2. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.
- What power supply and cooling does NVIDIA B200 need?
The NVIDIA B200 has a TDP of 1000 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 1600 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.
- Anything to watch out for with NVIDIA B200?
Datacenter accelerator — sold in HGX/NVL systems and typically rented in the cloud, not installed in a desktop. Listed for comparison.