Best AI Models for NVIDIA V100 SXM2 32GB (32.0GB)
32 GB positions this hardware in the professional tier for local AI. Most popular open-source models run comfortably, and even large 70B parameter models are accessible at lower quantization levels.
This memory amount is a sweet spot for enthusiasts and professionals. You can run 13B–30B models like DeepSeek R1 Distill at Q5 or Q6 quality with smooth token generation, and 7B models at near-lossless precision. The 70B class of models (Llama 3 70B, Qwen 72B) becomes possible at Q2–Q3 quantization, though with some quality trade-off. For day-to-day use with coding assistants, chat models, and reasoning tasks, this tier delivers an excellent experience.
Runs Well
- 7B–13B models at Q6–Q8 quality
- 14B–30B models at Q4–Q5 quality
- Small models (3B–7B) at FP16 precision
- Vision-language models at good quality
Challenging
- 70B models only at Q2–Q3 (noticeable quality loss)
- Large context windows with 30B+ models
What LLMs Can NVIDIA V100 SXM2 32GB Run?
108 models · 2 excellent · 22 good
Showing compatibility for NVIDIA V100 SXM2 32GB
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·61.4 t/s tok/s·16K ctx·FAIR FIT | Q4_K_M | 9.5 GB | 61.4 t/s | 16K | FAIR FIT | B45 |
Q4_K_M·72.8 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 8.0 GB | 72.8 t/s | 33K | EASY RUN | C40 |
Q4_K_M·55.8 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 10.5 GB | 55.8 t/s | 33K | FAIR FIT | B48 |
Q4_K_M·71.1 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 8.2 GB | 71.1 t/s | 262K | EASY RUN | C41 |
Q4_K_M·20.5 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 28.6 GB | 20.5 t/s | 33K | FAIR FIT | B56 |
Q4_K_M·72.5 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 8.1 GB | 72.5 t/s | 131K | EASY RUN | C40 |
Q4_K_M·68.1 t/s tok/s·EASY RUN | Q4_K_M | 8.6 GB | 68.1 t/s | — | EASY RUN | C42 |
Q4_K_M·61.4 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 9.5 GB | 61.4 t/s | 33K | FAIR FIT | B45 |
Q4_K_M·106.0 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 5.5 GB | 106.0 t/s | 41K | EASY RUN | C34 |
Q4_K_M·110.4 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 110.4 t/s | 131K | EASY RUN | C34 |
Q4_K_M·68.1 t/s tok/s·EASY RUN | Q4_K_M | 8.6 GB | 68.1 t/s | — | EASY RUN | C42 |
Q4_K_M·117.2 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 5.0 GB | 117.2 t/s | 33K | EASY RUN | C33 |
Q4_K_M·110.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 110.0 t/s | 131K | EASY RUN | C34 |
Q4_K_M·20.5 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 28.6 GB | 20.5 t/s | 33K | FAIR FIT | B56 |
Q4_K_M·68.2 t/s tok/s·EASY RUN | Q4_K_M | 8.6 GB | 68.2 t/s | — | EASY RUN | C42 |
Q4_K_M·118.9 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 118.9 t/s | 33K | EASY RUN | C33 |
NVIDIA V100 SXM2 32GB Specifications
- Brand
- NVIDIA
- Architecture
- Volta
- Compute Capability
- 7.0 (CUDA SM version)
- VRAM
- 32.0 GB HBM2
- Memory Bandwidth
- 900.0 GB/s
- CUDA Cores
- 5,120
- Tensor Cores
- 640
- FP16 Performance
- 125.00 TFLOPS
- TDP
- 300W
- Release Date
- 2018-03-27
Get Started
GPUs to Consider Over NVIDIA V100 SXM2 32GB
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
AMD Instinct MI210
AMD · CDNA 2
NVIDIA A100 40GB PCIe
NVIDIA · Ampere
NVIDIA RTX 6000 Ada Generation
NVIDIA · Ada Lovelace
AMD Radeon PRO W7900
AMD · RDNA 3
NVIDIA L40
NVIDIA · Ada Lovelace
Frequently Asked Questions
- Can NVIDIA V100 SXM2 32GB run Qwen3.6 35B A3B?
Yes, the NVIDIA V100 SXM2 32GB with 32 GB can run Qwen3.6 35B A3B, Gemma 4 31B IT, Qwen2.5 Coder 32B Instruct, and 1313 other models. 23 models run at excellent quality, and 182 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA V100 SXM2 32GB good for AI?
The NVIDIA V100 SXM2 32GB has 32 GB of HBM2, making it excellent for running local AI models. It supports 205 models at good quality or better. With 900.0 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 V100 SXM2 32GB handle?
With 32 GB, the NVIDIA V100 SXM2 32GB supports models from 3B to 30B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 53B parameters. This means 7B models at high quality (Q6/Q8) or 30B+ models at Q4.
- What quantization should I use on NVIDIA V100 SXM2 32GB?
For the best balance of quality and speed on the NVIDIA V100 SXM2 32GB, 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 V100 SXM2 32GB for AI inference?
With 900.0 GB/s memory bandwidth, the NVIDIA V100 SXM2 32GB achieves approximately 130 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~65 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (900 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA V100 SXM2 32GB
~27 tok/s~28 tok/s~29 tok/s~29 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA V100 SXM2 32GB?
The top-rated models for the NVIDIA V100 SXM2 32GB are Qwen3.6 35B A3B, Gemma 4 31B IT, Qwen2.5 Coder 32B Instruct. 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 V100 SXM2 32GB need?
The NVIDIA V100 SXM2 32GB has a TDP of 300 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 650 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.