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 107 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 72 models natively (vs 62), and exclusively fits 10 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) | 72 | 62 |
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) | 20.7 t/s(Q6_K) | 20.9 t/s(Q3_K_M) | +54%at Q3_K_M |
| Qwen 3.6 27B(27B) | 23.1 t/s(BF16) | 26 t/s(Q8_0) | +54%at Q8_0 |
| Llama 3.1 8B Instruct(8B) | 70 t/s(BF16) | 45.2 t/s(BF16) | +55% |
| Qwen 2.5 7B Instruct(7.6B) | 76.8 t/s(BF16) | 49.4 t/s(BF16) | +55% |
Delta is NVIDIA H100 80GB relative to NVIDIA RTX 6000 Ada, compared at the highest quantization that fits natively on both cards.
Only NVIDIA H100 80GB can run(10)
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(62)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct20.2 t/svs20.4 t/s
- Llama 3.3 70B Instruct20.7 t/svs20.9 t/s
- DeepSeek R1 Distill Llama 70B20.7 t/svs20.9 t/s
- Llama 3.1 70B Instruct20.7 t/svs20.9 t/s
- Mixtral 8x7B Instruct v0.175 t/svs60.9 t/s
- Flint 40B (MoE)162.9 t/svs147.7 t/s
- Command-R 35B27 t/svs20.4 t/s
- Qwen 3.5 35B-A3B (MoE)175.1 t/svs137.9 t/s
- Qwen 3.6 35B-A3B (MoE)175.1 t/svs137.9 t/s
- Ornith 1.5 35B-A3B (MoE)175.1 t/svs137.9 t/s
- Yi 1.5 34B Chat31.7 t/svs20.3 t/s
- Qwen3 32B19.1 t/svs21.4 t/s
- Qwen 2.5 32B Instruct19.1 t/svs21.2 t/s
- Qwen 2.5 Coder 32B Instruct19.1 t/svs21.2 t/s
- DeepSeek R1 Distill Qwen 32B19.1 t/svs21.2 t/s
- Nemotron 3 Nano 30B115.7 t/svs123.5 t/s
- +46 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:
- • Price or availability decides it: NVIDIA RTX 6000 Ada has no VRAM, bandwidth or backend advantage over NVIDIA H100 80GB
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 72 models natively in VRAM vs 62 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 10 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 20.7 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 20.9 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 72 models natively vs 62 for the NVIDIA RTX 6000 Ada.