NVIDIA RTX 6000 Ada
The NVIDIA RTX 6000 Ada has 48 GB VRAM and 960 GB/s memory bandwidth. It can run 62 of our 107 tracked models natively in VRAM at 8k context.
With 48 GB GDDR6, the NVIDIA RTX 6000 Ada is a workstation-tier GPU that can run 62 models natively.
The NVIDIA RTX 6000 Ada Generation is the Ada Lovelace successor to the RTX A6000, upgrading memory bandwidth from 768 to 960 GB/s while keeping the same 48GB GDDR6 VRAM and workstation form factor. The jump in bandwidth meaningfully improves inference tokens-per-second on larger models. Unlike its predecessor, it has no NVLink (NVIDIA's datasheet lists it as not supported), so a second card pools over PCIe 4.0 x16 only; it keeps ECC memory, a 300W board power, and fits in standard workstations, making it a top-tier single-GPU option for on-prem LLM workloads that need professional reliability.
NVIDIA RTX 6000 Ada: October 2022 Ada workstation with 48GB GDDR6 at 960 GB/s, A6000 successor.
70B at Q3_K_M native (Q4_K_M's ~51GB total is just past this card's effective capacity), with ~25% higher tokens/sec than A6000 at the same quant. ~30-45 t/s for 7B, ~17 t/s for 70B at Q3_K_M.
Full CUDA with ECC. No NVLink (NVIDIA's datasheet lists it as not supported), so multi-card setups pool over PCIe 4.0 x16. Top single-GPU option for on-prem LLM needing professional reliability.
| Vendor | NVIDIA |
| Architecture | Ada Lovelace |
| VRAM | 48 GB |
| Memory type | GDDR6 |
| Memory bandwidth | 960 GB/s |
| Compute backend | CUDA |
| Tier | Workstation |
| Released | 2022 |
| Models (native) | 62 / 107 |
| Models (offload) | 10 / 107 |
Cloud GPU Rental
Don't want to buy an NVIDIA RTX 6000 Ada? RunPod is a cloud GPU rental service: rent one by the hour instead, no contract, no upfront hardware cost.
Pay by the hour · no contract · pods start in about a minute.
Rent an RTX 6000 Ada from $0.74/hr on RunPod ↗ (+$5 signup credit)Prices as of Oct 17, 2026. Rates change; check RunPod for current pricing.
Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.
Popular models for this GPU
Models this GPU runs natively in VRAM (62)
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q3_K_M · ~20.4 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q3_K_M · ~20.9 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q3_K_M · ~20.9 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q3_K_M · ~20.9 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q6_K · ~60.9 t/s
Show 57 more
- Flint 40B (MoE)39.8B · MMLU-Pro N/AQ6_K · ~147.7 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q6_K · ~20.4 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q8_0 · ~137.9 t/s
- Qwen 3.6 35B-A3B (MoE)35B · MMLU-Pro 85.2Q8_0 · ~137.9 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ8_0 · ~137.9 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~20.3 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~21.4 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~21.2 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q8_0 · ~21.2 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~21.2 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~123.5 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q8_0 · ~22.7 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~115.4 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q8_0 · ~118.1 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ8_0 · ~26.1 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~24.6 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~25.9 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~26 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q8_0 · ~26 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~76.9 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~87.3 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ8_0 · ~26 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q8_0 · ~130.1 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~29.3 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~29.8 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~148.4 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~25.6 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~25.3 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~26.6 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~30.4 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~30.6 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~31.4 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~36.2 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~42.5 t/s
- Bonsai 2 9B9B · MMLU-Pro N/ATernary (Q2_0) · ~189.8 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~42.5 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~45.2 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~45.2 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~44.4 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~49.4 t/s
- Gemma 4 E4B7.52B · MMLU-Pro 69.4BF16 · ~74.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~49.1 t/s
- Gemma 4 E2B4.65B · MMLU-Pro 60.0BF16 · ~131.4 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~84.3 t/s
- Bonsai 2 4B4B · MMLU-Pro N/ATernary (Q2_0) · ~272.5 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~67.9 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~82.1 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~96.5 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~101.5 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~114.3 t/s
- Bonsai 2 2B2B · MMLU-Pro N/ATernary (Q2_0) · ~452.4 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~135.7 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~181.5 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~237.3 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~231.8 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~364.6 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~315.5 t/s
Models that fit with CPU offload (10)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q2_K · ~6.8 t/s
- Mistral Medium 3.5 128B128B · MMLU-Pro N/AQ2_K · ~3.3 t/s
- Ornith 1.5 122B (MoE)124.6B · MMLU-Pro N/AQ3_K_M · ~24.2 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q3_K_M · ~25 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q2_K · ~25.1 t/s
- Nemotron 3.5 Super VL 120B-A12B120B · MMLU-Pro N/AQ3_K_M · ~20.4 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q3_K_M · ~38.9 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q3_K_M · ~15 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q3_K_M · ~25 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q3_K_M · ~25 t/s
Too large for this GPU (35)
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GLM-4.5 355B
- GLM-4.6 355B
- GLM-4.7 358B
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- DeepSeek V4 Flash 0731 284B
- Qwen3.8 2.4T-A95B
- Qwen3.8-Flash-Next
- DeepSeek V4 Pro 0813 1.6T
- Muse Spark Open 816B-A42B
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
- DeepSeek V4.1 Flash 552B
- DeepSeek V4.1 Flash 1017 552B
Compare NVIDIA RTX 6000 Ada with other GPUs
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Frequently asked questions
- How much VRAM does the NVIDIA RTX 6000 Ada have?
- The NVIDIA RTX 6000 Ada has 48 GB of GDDR6 with 960 GB/s memory bandwidth.
- What is the NVIDIA RTX 6000 Ada best for?
- With 48 GB of VRAM, the NVIDIA RTX 6000 Ada is ideal for running 70B-class models at Q3-class quantization and large MoE models, a workstation sweet spot for local inference.
- What LLMs can the NVIDIA RTX 6000 Ada run locally?
- The NVIDIA RTX 6000 Ada can run 62 of the 107 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q8_0, Ornith 1.5 35B-A3B (MoE) at Q8_0, Ornith 1.5 9B at BF16.
- Can the NVIDIA RTX 6000 Ada run Gemma 4 31B?
- Yes. The NVIDIA RTX 6000 Ada runs Gemma 4 31B natively in VRAM at Q8_0 quantization, achieving approximately 22.7 tokens per second.
- Can the NVIDIA RTX 6000 Ada run Qwen 3.6 27B?
- Yes. The NVIDIA RTX 6000 Ada runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 26 tokens per second.
- Can the NVIDIA RTX 6000 Ada run Qwen3 8B?
- Yes. The NVIDIA RTX 6000 Ada runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 44.4 tokens per second.
- Can I rent the NVIDIA RTX 6000 Ada instead of buying it?
- Yes: RunPod and similar cloud GPU providers let you rent NVIDIA RTX 6000 Ada instances by the hour, with no long-term contract. Rentals start from $0.74/hr on RunPod (as of Oct 17, 2026). This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.