NVIDIA - workstation GPU - verified
Local LLMs that run on NVIDIA RTX 6000 Ada Generation (48 GB)
By this directory's rule of thumb, 32 of 63 open-weight models fit in the 48 GB of VRAM on the RTX 6000 Ada Generation. The largest by total parameters is NVIDIA Nemotron 3.5 Bolt 74B-A9B (74.9B), whose smallest non-MLX download is 22.0 GB, the 24GB (native 2-bit) GGUF build from nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF.
- Models that fit
- 32 of 63
- Memory
- 48 GB
- Memory type
- GDDR6 ECC
- Bandwidth
- 960 GB/s
Spec sheet
- Memory
- 48 GB
- Memory type
- GDDR6 with ECC
- Unified memory
- No
- Bandwidth
- 960 GB/s
The product page lists 48GB GDDR6 with ECC but no bandwidth, so the 960 GB/s figure and 384-bit interface come from the official NVIDIA datasheet linked here.
Source: NVIDIA spec page (nvidia.com)
32 models that fit in 48 GB
Total params, largest first| Model | Total params | Smallest download | Quant | Commercial |
|---|---|---|---|---|
| NVIDIA Nemotron 3.5 Bolt 74B-A9BNVIDIA | 74.9B9B active | 22.0 GB | 24GB (native 2-bit)GGUF | Commercial use: Yes |
| AI21 Jamba2 MiniAI21 Labs | 51.6B12B active | 31.2 GB | Q4_K_MGGUF | Commercial use: Yes |
| Qwen3.6-35B-A3BAlibaba Cloud (Qwen) | 36B3B active | 22.1 GB | UD-Q4_K_MGGUF | Commercial use: Yes |
| Cohere Aya Expanse 32BCohere | 32.3B | 19.8 GB | Q4_K_MGGUF | Commercial use: No |
| Ai2 Olmo 3.1 32B ThinkAi2 | 32.2B | 19.5 GB | Q4_K_MGGUF | Commercial use: Yes |
| NVIDIA Nemotron 3.5 Lightning 30B-A3BNVIDIA | 31.6B3B active | 18.9 GB | Q4_0GGUF | Commercial use: Yes |
| Gemma 4 31BGoogle | 31.3B | 17.7 GB | Q4_0 (QAT)GGUF | Commercial use: Yes |
| IBM Granite 4.2 30BIBM | 29.3B | 17.7 GB | Q4_K_MGGUF | Commercial use: Yes |
| Qwen3.8-27BAlibaba Cloud (Qwen) | 27.8B | 16.5 GB | UD-Q4_K_MGGUF | Commercial use: Yes |
| Gemma 4 26B A4BGoogle | 25.8B3.8B active | 14.4 GB | Q4_0 (QAT)GGUF | Commercial use: Yes |
| Devstral Small 2 24B Instruct 2512Mistral AI | 24B | 14.3 GB | Q4_K_MGGUF | Commercial use: Yes |
| Liquid AI LFM2.5-24B-A2BLiquid AI | 24B2.5B active | 14.5 GB | Q4_K_MGGUF | Commercial use: Conditions |
| gpt-oss-20bOpenAI | 20.9B3.6B active | 11.6 GB | Q4_K_MGGUF | Commercial use: Yes |
| Phi-4-reasoning-vision-15BMicrosoft | 15.1B | 30.2 GB | BF16Official weights | Commercial use: Yes |
| Phi-4Microsoft | 14.7B | 9.1 GB | Q4_KGGUF | Commercial use: Yes |
| Phi-4-reasoning-plusMicrosoft | 14.7B | 9.1 GB | Q4_K_MGGUF | Commercial use: Yes |
| Ministral 3 14B Instruct 2512Mistral AI | 13.9B | 8.2 GB | Q4_K_MGGUF | Commercial use: Yes |
| Gemma 4 12BGoogle | 12B | 7.0 GB | Q4_0 (QAT)GGUF | Commercial use: Yes |
| Qwen3.5-9BAlibaba Cloud (Qwen) | 9.7B | 5.7 GB | Q4_K_MGGUF | Commercial use: Yes |
| Ministral 3 8B Instruct 2512Mistral AI | 8.9B | 5.2 GB | Q4_K_MGGUF | Commercial use: Yes |
| IBM Granite 4.2 8BIBM | 8.8B | 5.3 GB | Q4_K_MGGUF | Commercial use: Yes |
| Cohere Command R7B (12-2024)Cohere | 8B | 5.1 GB | Q4_K_MGGUF | Commercial use: No |
| Gemma 4 E4BGoogle | 8B | 5.0 GB | Q4_K_MGGUF | Commercial use: Yes |
| Ai2 Olmo Hybrid Instruct DPO 7BAi2 | 7.4B | 14.9 GB | BF16Official weights | Commercial use: Yes |
| Gemma 4 E2BGoogle | 5.1B | 3.1 GB | Q4_K_MGGUF | Commercial use: Yes |
| Qwen3.5-4BAlibaba Cloud (Qwen) | 4.7B | 2.7 GB | Q4_K_MGGUF | Commercial use: Yes |
| Ministral 3 3B Instruct 2512Mistral AI | 3.8B | 2.1 GB | Q4_K_MGGUF | Commercial use: Yes |
| Phi-4-mini-instructMicrosoft | 3.8B | 2.5 GB | Q4_K_MGGUF | Commercial use: Yes |
| Phi-4-mini-reasoningMicrosoft | 3.8B | 2.5 GB | Q4_K_MGGUF | Commercial use: Yes |
| Liquid AI LFM2.5-2.6BLiquid AI | 2.7B | 1.7 GB | Q4_K_MGGUF | Commercial use: Conditions |
| Qwen3.5-2BAlibaba Cloud (Qwen) | 2.3B | 1.3 GB | Q4_K_MGGUF | Commercial use: Yes |
| Qwen3.5-0.8BAlibaba Cloud (Qwen) | 0.9B | 0.5 GB | Q4_K_MGGUF | Commercial use: Yes |
The rest of the 63 need more than 48 GB. Browse every local model.
Other devices with 48 GB
Same memory size. Each shows how many models fit under its own rule (Macs also count MLX builds), and the memory type and bandwidth differ.
Compare all 33 GPUs and Macs by memory, bandwidth and how many models fit.
FAQ
How many local LLMs can the RTX 6000 Ada Generation run?
32 of the 63 open-weight models in this directory fit, with 48 GB of VRAM, by this site's rule of thumb that the smallest non-MLX download is at most 85% of the memory. MLX builds do not count here: they run on Apple silicon only.
Can the RTX 6000 Ada Generation run NVIDIA Nemotron 3.5 Bolt 74B-A9B?
Yes, by this directory's rule of thumb: its smallest non-MLX download, 22.0 GB, is at most 85% of 48 GB (40.8 GB), the 24GB (native 2-bit) GGUF build from nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF. That leaves room for the context. It is a memory check, not a speed estimate.
Can the RTX 6000 Ada Generation run Liquid AI LFM2.5-24B-A2B?
Yes, by this directory's rule of thumb: its smallest non-MLX download, 14.5 GB, is at most 85% of 48 GB (40.8 GB), the Q4_K_M GGUF build from LiquidAI/LFM2.5-24B-A2B-GGUF. That leaves room for the context. It is a memory check, not a speed estimate.
Can the RTX 6000 Ada Generation run Qwen3-Coder-Next?
Not by this directory's rule of thumb: its smallest non-MLX download, 48.4 GB, is more than 85% of 48 GB (40.8 GB). It needs the 64 GB tier or more: https://www.developersdigest.tech/local-models/fits/64.
Can the RTX 6000 Ada Generation use system RAM for bigger models?
This page only counts models that fit entirely in the 48 GB of VRAM. Runtimes can offload layers to system RAM, but this directory does not list or measure that.
What memory bandwidth does the RTX 6000 Ada Generation have?
NVIDIA states 960 GB/s of memory bandwidth (GDDR6 with ECC). From the spec notes: The product page lists 48GB GDDR6 with ECC but no bandwidth, so the 960 GB/s figure and 384-bit interface come from the official NVIDIA datasheet linked here. This page does not turn bandwidth into a speed estimate.
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