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 70 open-weight models.
Quick verdict
NVIDIA H100 80GB wins for local AI inference. It has 32 GB more VRAM and 249% more memory bandwidth, runs 54 models natively (vs 53), and exclusively fits 1 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) | 54 | 53 |
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) | 63.8 t/s(Q6_K) | 27.4 t/s(Q4_K_M) | +133% |
| Qwen 3.6 27B(27B) | 62 t/s(FP16) | 35.6 t/s(Q8) | +74% |
| Llama 3.1 8B Instruct(8B) | 209.4 t/s(FP16) | 60 t/s(FP16) | +249% |
| Qwen 2.5 7B Instruct(7.6B) | 220.4 t/s(FP16) | 63.2 t/s(FP16) | +249% |
Delta is NVIDIA H100 80GB relative to NVIDIA RTX 6000 Ada.
Only NVIDIA H100 80GB can run(1)
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(53)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 122B-A10B (MoE)737 t/svs352 t/s
- Nemotron 3 Super 120B614.2 t/svs293.3 t/s
- GPT-OSS 120B1474 t/svs704 t/s
- Llama 4 Scout 109B433.5 t/svs207.1 t/s
- GLM-4.5 Air 106B491.3 t/svs293.3 t/s
- GLM-4.6V 106B491.3 t/svs293.3 t/s
- Qwen 2.5 72B Instruct62 t/svs26.7 t/s
- Llama 3.3 70B Instruct63.8 t/svs27.4 t/s
- DeepSeek R1 Distill Llama 70B63.8 t/svs27.4 t/s
- Llama 3.1 70B Instruct63.8 t/svs27.4 t/s
- Mixtral 8x7B Instruct v0.1285.7 t/svs109.1 t/s
- Command-R 35B95.7 t/svs36.6 t/s
- Qwen 3.5 35B-A3B (MoE)1228.3 t/svs352 t/s
- Qwen 3.6 35B95.7 t/svs27.4 t/s
- Yi 1.5 34B Chat97.4 t/svs27.9 t/s
- Qwen3 32B51.1 t/svs29.3 t/s
- +37 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 54 models natively in VRAM vs 53 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 1 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 63.8 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 Q4_K_M quantization at approximately 27.4 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 54 models natively vs 53 for the NVIDIA RTX 6000 Ada.