NVIDIA RTX Pro 6000 vs NVIDIA L40S
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 99 open-weight models.
Quick verdict
NVIDIA RTX Pro 6000 wins for local AI inference. It has 48 GB more VRAM and 107.4% more memory bandwidth, runs 69 models natively (vs 58), and exclusively fits 11 models the other cannot.
Analysis
The NVIDIA L40S was the data center's answer to the 48 GB workstation gap when it launched in late 2023. The RTX Pro 6000 Blackwell, arriving two years later, turns that comparison on its head: twice the VRAM and roughly twice the bandwidth.
Head-to-head, the Pro 6000 leads on every spec directly relevant to LLM inference: 96 GB vs 48 GB VRAM, 1,792 vs 864 GB/s bandwidth, and Blackwell architecture vs Ada. The L40S does offer passive cooling in a dual-slot PCIe card optimized for rack-mount servers, and it adds hardware-accelerated video encode/decode that matters for mixed multimedia and inference workloads. The L40S also has an established support footprint in cloud and colocation infrastructure. But for new on-prem purchases purely focused on LLM inference throughput, the Pro 6000 delivers a decisive capacity and speed advantage: Qwen 3.6 27B at Q4_K_M runs 68.5 tok/s on it versus 30.6 tok/s on the L40S.
Bottom line: Existing L40S deployments are not urgently due for replacement: the card still handles 70B models well and cloud providers will run it for years. For new on-prem hardware decisions in 2025, the RTX Pro 6000 is the better investment: it runs the same 70B models with more headroom and more than twice the decode speed. The L40S remains relevant in environments where rack-mount form factor, video processing pipelines, or existing datacenter contracts drive the decision.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | NVIDIA L40S |
|---|---|---|
| VRAM | 96 GB | 48 GB |
| Memory type | GDDR7 | GDDR6 |
| Bandwidth | 1792 GB/s(+107%) | 864 GB/s |
| Architecture | Blackwell | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Workstation | Datacenter |
| Released | 2025 | 2023 |
| Models (native) | 69 | 58 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX Pro 6000 | NVIDIA L40S | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 34.8 t/s(NVFP4) | 15 t/s(Q3_K_M) | +139%at Q3_K_M |
| Qwen 3.6 27B(27B) | 26 t/s(BF16) | 18.7 t/s(Q8_0) | +139%at Q8_0 |
| Llama 3.1 8B Instruct(8B) | 78.4 t/s(BF16) | 32.3 t/s(BF16) | +143% |
| Qwen 2.5 7B Instruct(7.6B) | 86.1 t/s(BF16) | 35.3 t/s(BF16) | +144% |
Delta is NVIDIA RTX Pro 6000 relative to NVIDIA L40S, compared at the highest quantization that fits natively on both cards.
Only NVIDIA RTX Pro 6000 can run(11)
Only NVIDIA L40S can run(0)
No exclusive models: NVIDIA RTX Pro 6000 can run everything NVIDIA L40S can.
Both run natively(58)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct34.1 t/svs14.7 t/s
- Llama 3.3 70B Instruct34.8 t/svs15 t/s
- DeepSeek R1 Distill Llama 70B34.8 t/svs15 t/s
- Llama 3.1 70B Instruct34.8 t/svs15 t/s
- Mixtral 8x7B Instruct v0.1135.2 t/svs44.5 t/s
- Command-R 35B18.7 t/svs14.4 t/s
- Qwen 3.5 35B-A3B (MoE)139.1 t/svs111.3 t/s
- Qwen 3.6 35B-A3B (MoE)139.1 t/svs111.3 t/s
- Ornith 1.5 35B-A3B (MoE)139.1 t/svs111.3 t/s
- Yi 1.5 34B Chat20.6 t/svs14.5 t/s
- Qwen3 32B21.6 t/svs15.3 t/s
- Qwen 2.5 32B Instruct21.5 t/svs15.1 t/s
- Qwen 2.5 Coder 32B Instruct21.5 t/svs15.1 t/s
- DeepSeek R1 Distill Qwen 32B21.5 t/svs15.1 t/s
- Nemotron 3 Nano 30B123.4 t/svs102.4 t/s
- Gemma 4 31B23 t/svs16.2 t/s
- +42 more on both
Which should you choose?
- • You need to run larger models (>48 GB VRAM)
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
- • Price or availability decides it: NVIDIA L40S has no VRAM, bandwidth or backend advantage over NVIDIA RTX Pro 6000
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX Pro 6000 or NVIDIA L40S?
- For local AI inference, the NVIDIA RTX Pro 6000 has the edge. It offers 96 GB VRAM (vs 48 GB) and 1792 GB/s bandwidth (vs 864 GB/s), letting it run 69 models natively in VRAM vs 58 for its rival.
- How much VRAM does the NVIDIA RTX Pro 6000 have vs the NVIDIA L40S?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1792 GB/s. The NVIDIA L40S has 48 GB of GDDR6 at 864 GB/s. The NVIDIA RTX Pro 6000 has 48 GB more VRAM, allowing it to run 11 models the NVIDIA L40S cannot fit natively.
- Can the NVIDIA RTX Pro 6000 run Llama 3.3 70B?
- Yes. The NVIDIA RTX Pro 6000 runs Llama 3.3 70B natively at NVFP4 quantization at approximately 34.8 tokens per second.
- Can the NVIDIA L40S run Llama 3.3 70B?
- Yes. The NVIDIA L40S runs Llama 3.3 70B natively at Q3_K_M quantization at approximately 15 tokens per second.
- What is the difference between the NVIDIA RTX Pro 6000 and NVIDIA L40S for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX Pro 6000 has 96 GB VRAM at 1792 GB/s (CUDA backend). The NVIDIA L40S has 48 GB VRAM at 864 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX Pro 6000 runs 69 models natively vs 58 for the NVIDIA L40S.