NVIDIA RTX 5000 Ada
The NVIDIA RTX 5000 Ada has 32 GB VRAM and 576 GB/s memory bandwidth. It can run 54 of our 99 tracked models natively in VRAM at 8k context.
With 32 GB GDDR6, the NVIDIA RTX 5000 Ada is a workstation-tier GPU that can run 54 models natively.
The NVIDIA RTX 5000 Ada fills the gap between the RTX 4500 Ada and RTX 6000 Ada, featuring 32GB ECC GDDR6 on a 256-bit bus at 576 GB/s with 12,800 CUDA cores. This VRAM headroom enables 34B models at Q4_K_M and most 27B models at Q8_0 to run entirely in memory. Professional ECC memory and certified drivers make it a reliable choice for on-prem AI inference deployments.
NVIDIA RTX 5000 Ada: 2023 Ada Lovelace workstation card bridging the RTX 4500 Ada and RTX 6000 Ada: 32GB ECC GDDR6 on a 256-bit bus at 576 GB/s.
34B models fit at Q4_K_M; most 27B models fit at Q8_0 entirely in VRAM. ~22-30 t/s for 7B Q4.
Full CUDA with ECC and certified drivers. A reliable middle ground for on-prem inference deployments that outgrow 24GB but don't need the RTX 6000 Ada's 48GB.
| Vendor | NVIDIA |
| Architecture | Ada Lovelace |
| VRAM | 32 GB |
| Memory type | GDDR6 |
| Memory bandwidth | 576 GB/s |
| Compute backend | CUDA |
| Tier | Workstation |
| Released | 2023 |
| Models (native) | 54 / 99 |
| Models (offload) | 9 / 99 |
Cloud GPU Rental
Don't want to buy an NVIDIA RTX 5000 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 5000 Ada from $0.49/hr on RunPod ↗ (+$5 signup credit)Prices as of Oct 13, 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 (54)
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q3_K_M · ~58.9 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q2_K · ~20 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q5_K_M · ~127.8 t/s
- Qwen 3.6 35B-A3B (MoE)35B · MMLU-Pro 85.2Q5_K_M · ~127.8 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ5_K_M · ~127.8 t/s
Show 49 more
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q5_K_M · ~17.9 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q5_K_M · ~19 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q5_K_M · ~18.6 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q5_K_M · ~18.6 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q5_K_M · ~18.6 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q6_K · ~108.4 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q6_K · ~17.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q6_K · ~96.3 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q5_K_M · ~103.3 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ6_K · ~20.6 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q6_K · ~18.9 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q6_K · ~20.2 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q6_K · ~20.6 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q6_K · ~20.6 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~52.3 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~60.5 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ6_K · ~20.6 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q6_K · ~106.8 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~18.1 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~18.6 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~100.8 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q8_0 · ~27.7 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q8_0 · ~27.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q8_0 · ~28.5 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~18.8 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~19 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~19.5 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~22.5 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~26.3 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~26.3 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~28.1 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~28.1 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~27.7 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~30.7 t/s
- Gemma 4 E4B7.52B · MMLU-Pro 69.4BF16 · ~48.6 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~30.6 t/s
- Gemma 4 E2B4.65B · MMLU-Pro 60.0BF16 · ~88.6 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~53.8 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~43 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~52.5 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~61.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~66.7 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~73.9 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~88.3 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~124 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~156.7 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~162.6 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~276.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~249.8 t/s
Models that fit with CPU offload (9)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q2_K · ~22.9 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q2_K · ~17.7 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q2_K · ~12.6 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q2_K · ~20.2 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q2_K · ~20.2 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q4_K_M · ~1.4 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q4_K_M · ~1.5 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q4_K_M · ~1.5 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q4_K_M · ~1.5 t/s
Too large for this GPU (36)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- 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
- Mistral Medium 3.5 128B
- 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
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
- DeepSeek V4.1 Flash 552B
Frequently asked questions
- How much VRAM does the NVIDIA RTX 5000 Ada have?
- The NVIDIA RTX 5000 Ada has 32 GB of GDDR6 with 576 GB/s memory bandwidth.
- What is the NVIDIA RTX 5000 Ada best for?
- With 32 GB of VRAM, the NVIDIA RTX 5000 Ada is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the NVIDIA RTX 5000 Ada run locally?
- The NVIDIA RTX 5000 Ada can run 54 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q6_K, Ornith 1.5 35B-A3B (MoE) at Q5_K_M, Ornith 1.5 9B at BF16.
- Can the NVIDIA RTX 5000 Ada run Gemma 4 31B?
- Yes. The NVIDIA RTX 5000 Ada runs Gemma 4 31B natively in VRAM at Q6_K quantization, achieving approximately 17.8 tokens per second.
- Can the NVIDIA RTX 5000 Ada run Qwen 3.6 27B?
- Yes. The NVIDIA RTX 5000 Ada runs Qwen 3.6 27B natively in VRAM at Q6_K quantization, achieving approximately 20.6 tokens per second.
- Can the NVIDIA RTX 5000 Ada run Qwen3 8B?
- Yes. The NVIDIA RTX 5000 Ada runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 27.7 tokens per second.
- Can I rent the NVIDIA RTX 5000 Ada instead of buying it?
- Yes: RunPod and similar cloud GPU providers let you rent NVIDIA RTX 5000 Ada instances by the hour, with no long-term contract. Rentals start from $0.49/hr on RunPod (as of Oct 13, 2026). This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.