NVIDIA H100 80GB vs NVIDIA RTX 6000 Ada
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 97 open-weight models.
Quick verdict
NVIDIA H100 80GB wins for local AI inference. It has 32 GB more VRAM and 249.0% more memory bandwidth, runs 65 models natively (vs 57), and exclusively fits 8 models the other cannot.
Specs comparison
| Spec | NVIDIA H100 80GB | NVIDIA RTX 6000 Ada |
|---|---|---|
| VRAM | 80 GB | 48 GB |
| Memory type | HBM3 | GDDR6 |
| Bandwidth | 3350 GB/s(+249%) | 960 GB/s |
| Architecture | Hopper | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Datacenter | Workstation |
| Released | 2022 | 2022 |
| Models (native) | 65 | 57 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA H100 80GB | NVIDIA RTX 6000 Ada | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 36.2 t/s(Q6_K) | 17.2 t/s(Q3_K_M) | +110% |
| Qwen 3.6 27B(27B) | 39.9 t/s(BF16) | 21.3 t/s(Q8_0) | +87% |
| Llama 3.1 8B Instruct(8B) | 127.5 t/s(BF16) | 36.5 t/s(BF16) | +249% |
| Qwen 2.5 7B Instruct(7.6B) | 139 t/s(BF16) | 39.8 t/s(BF16) | +249% |
Delta is NVIDIA H100 80GB relative to NVIDIA RTX 6000 Ada.
Only NVIDIA H100 80GB can run(8)
Only NVIDIA RTX 6000 Ada can run(0)
No exclusive models: NVIDIA H100 80GB can run everything NVIDIA RTX 6000 Ada can.
Both run natively(57)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct35.2 t/svs16.7 t/s
- Llama 3.3 70B Instruct36.2 t/svs17.2 t/s
- DeepSeek R1 Distill Llama 70B36.2 t/svs17.2 t/s
- Llama 3.1 70B Instruct36.2 t/svs17.2 t/s
- Mixtral 8x7B Instruct v0.146.5 t/svs17.2 t/s
- Command-R 35B45.4 t/svs15.8 t/s
- Qwen 3.5 35B-A3B (MoE)201.7 t/svs57.8 t/s
- Qwen 3.6 35B55.3 t/svs15.9 t/s
- Ornith 1.5 35B-A3B (MoE)201.7 t/svs57.8 t/s
- Yi 1.5 34B Chat56.4 t/svs16.2 t/s
- Qwen3 32B32.5 t/svs17.2 t/s
- Qwen 2.5 32B Instruct32.4 t/svs17 t/s
- Qwen 2.5 Coder 32B Instruct32.4 t/svs17 t/s
- DeepSeek R1 Distill Qwen 32B32.4 t/svs17 t/s
- Nemotron 3 Nano 30B106.6 t/svs56.4 t/s
- Gemma 4 31B34.6 t/svs18.3 t/s
- +41 more on both
Which should you choose?
Choose NVIDIA H100 80GB if:
- • You need to run larger models (>48 GB VRAM)
- • Faster token generation is the priority
Choose NVIDIA RTX 6000 Ada if:
Frequently asked questions
- Which is better for local AI, the NVIDIA H100 80GB or NVIDIA RTX 6000 Ada?
- For local AI inference, the NVIDIA H100 80GB has the edge. It offers 80 GB VRAM (vs 48 GB) and 3350 GB/s bandwidth (vs 960 GB/s), letting it run 65 models natively in VRAM vs 57 for its rival.
- How much VRAM does the NVIDIA H100 80GB have vs the NVIDIA RTX 6000 Ada?
- The NVIDIA H100 80GB has 80 GB of HBM3 at 3350 GB/s. The NVIDIA RTX 6000 Ada has 48 GB of GDDR6 at 960 GB/s. The NVIDIA H100 80GB has 32 GB more VRAM, allowing it to run 8 models the NVIDIA RTX 6000 Ada cannot fit natively.
- Can the NVIDIA H100 80GB run Llama 3.3 70B?
- Yes. The NVIDIA H100 80GB runs Llama 3.3 70B natively at Q6_K quantization at approximately 36.2 tokens per second.
- Can the NVIDIA RTX 6000 Ada run Llama 3.3 70B?
- Yes. The NVIDIA RTX 6000 Ada runs Llama 3.3 70B natively at Q3_K_M quantization at approximately 17.2 tokens per second.
- What is the difference between the NVIDIA H100 80GB and NVIDIA RTX 6000 Ada for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA H100 80GB has 80 GB VRAM at 3350 GB/s (CUDA backend). The NVIDIA RTX 6000 Ada has 48 GB VRAM at 960 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA H100 80GB runs 65 models natively vs 57 for the NVIDIA RTX 6000 Ada.