Quick Answer: NVIDIA RTX 6000 Ada offers 48GB VRAM and starts around $6765.41. It delivers approximately 245 tokens/sec on deepseek-ai/DeepSeek-OCR. It typically draws 300W under load.
This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
With 48GB VRAM, NVIDIA RTX 6000 Ada can run models up to approximately 120B parameters using 4-bit quantization. This handles most popular models including Llama 3 70B, Mistral 7B, and larger.
Consider H100 or MI300X — Maximum VRAM for enterprise workloads.
| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| deepseek-ai/DeepSeek-OCR | Q4 | 244.96 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 234.89 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 234.09 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 232.31 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 229.82 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 229.13 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR-2 | Q4 | 227.53 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 227.15 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 226.73 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 226.51 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 225.92 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 224.30 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 223.57 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 223.15 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 222.52 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-ASR-1.7B | Q4 | 219.96 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B | Q4 | 217.82 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 217.71 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 217.54 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 217.33 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 214.19 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 208.02 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 206.41 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 204.48 tok/sEstimated Auto-generated benchmark | 1GB |
| nineninesix/kani-tts-2-en | Q4 | 203.62 tok/sEstimated Auto-generated benchmark | 1GB |
| inference-net/Schematron-3B | Q4 | 203.44 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 203.40 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 201.76 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 200.88 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 200.54 tok/sEstimated Auto-generated benchmark | 2GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 200.09 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 199.71 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 197.64 tok/sEstimated Auto-generated benchmark | 2GB |
| skt/kogpt2-base-v2 | Q4 | 195.55 tok/sEstimated Auto-generated benchmark | 4GB |
| distilbert/distilgpt2 | Q4 | 194.83 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B | Q4 | 194.27 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2-0.5B-Instruct | Q4 | 194.24 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen2.5-Coder-1.5B | Q4 | 194.11 tok/sEstimated Auto-generated benchmark | 3GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 193.95 tok/sEstimated Auto-generated benchmark | 4GB |
| rednote-hilab/dots.ocr | Q4 | 193.84 tok/sEstimated Auto-generated benchmark | 4GB |
| numind/NuExtract-1.5 | Q4 | 193.67 tok/sEstimated Auto-generated benchmark | 4GB |
| liuhaotian/llava-v1.5-7b | Q4 | 193.60 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-3-1b-it | Q4 | 192.86 tok/sEstimated Auto-generated benchmark | 1GB |
| zai-org/GLM-OCR | Q4 | 192.82 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 192.74 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | 192.16 tok/sEstimated Auto-generated benchmark | 3GB |
| nvidia/personaplex-7b-v1 | Q4 | 192.12 tok/sEstimated Auto-generated benchmark | 4GB |
| vikhyatk/moondream2 | Q4 | 191.64 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Reranker-0.6B | Q4 | 191.20 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 190.50 tok/sEstimated Auto-generated benchmark | 2GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| EssentialAI/rnj-1 | FP16 | Fits comfortably | 46.16 tok/sEstimated | 19GB (have 48GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 181.18 tok/sEstimated | 4GB (have 48GB) |
| openai-community/gpt2 | Q8 | Fits comfortably | 129.29 tok/sEstimated | 7GB (have 48GB) |
| openai-community/gpt2 | FP16 | Fits comfortably | 70.38 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 188.19 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen2.5-7B-Instruct | Q8 | Fits comfortably | 133.87 tok/sEstimated | 7GB (have 48GB) |
| Qwen/Qwen2.5-7B-Instruct | FP16 | Fits comfortably | 73.52 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen3-0.6B | Q4 | Fits comfortably | 164.62 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen3-0.6B | Q8 | Fits comfortably | 125.57 tok/sEstimated | 6GB (have 48GB) |
| Qwen/Qwen3-0.6B | FP16 | Fits comfortably | 72.54 tok/sEstimated | 13GB (have 48GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q4 | Fits comfortably | 183.95 tok/sEstimated | 3GB (have 48GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 121.10 tok/sEstimated | 5GB (have 48GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 66.69 tok/sEstimated | 11GB (have 48GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 173.06 tok/sEstimated | 4GB (have 48GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 118.71 tok/sEstimated | 9GB (have 48GB) |
| meta-llama/Llama-3.1-8B-Instruct | FP16 | Fits comfortably | 65.32 tok/sEstimated | 17GB (have 48GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q4 | Fits comfortably | 67.27 tok/sEstimated | 17GB (have 48GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | Q8 | Fits comfortably | 47.19 tok/sEstimated | 35GB (have 48GB) |
| dphn/dolphin-2.9.1-yi-1.5-34b | FP16 | Not supported | 21.52 tok/sEstimated | 70GB (have 48GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 91.11 tok/sEstimated | 10GB (have 48GB) |
| openai/gpt-oss-20b | Q8 | Fits comfortably | 66.62 tok/sEstimated | 20GB (have 48GB) |
| openai/gpt-oss-20b | FP16 | Fits comfortably | 40.65 tok/sEstimated | 41GB (have 48GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 192.86 tok/sEstimated | 1GB (have 48GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 146.92 tok/sEstimated | 1GB (have 48GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 84.56 tok/sEstimated | 2GB (have 48GB) |
| Qwen/Qwen3-Embedding-0.6B | Q4 | Fits comfortably | 182.68 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen3-Embedding-0.6B | Q8 | Fits comfortably | 131.28 tok/sEstimated | 6GB (have 48GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 72.08 tok/sEstimated | 13GB (have 48GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 192.16 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 123.62 tok/sEstimated | 5GB (have 48GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 68.38 tok/sEstimated | 11GB (have 48GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 176.93 tok/sEstimated | 4GB (have 48GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 122.62 tok/sEstimated | 7GB (have 48GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits comfortably | 73.87 tok/sEstimated | 15GB (have 48GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 160.69 tok/sEstimated | 2GB (have 48GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 116.17 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 66.68 tok/sEstimated | 9GB (have 48GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 229.82 tok/sEstimated | 1GB (have 48GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q8 | Fits comfortably | 161.54 tok/sEstimated | 1GB (have 48GB) |
| meta-llama/Llama-3.2-1B-Instruct | FP16 | Fits comfortably | 85.55 tok/sEstimated | 2GB (have 48GB) |
| openai/gpt-oss-120b | Q4 | Not supported | 38.49 tok/sEstimated | 59GB (have 48GB) |
| openai/gpt-oss-120b | Q8 | Not supported | 26.50 tok/sEstimated | 117GB (have 48GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 14.72 tok/sEstimated | 235GB (have 48GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 201.76 tok/sEstimated | 2GB (have 48GB) |
| Qwen/Qwen2.5-3B-Instruct | Q8 | Fits comfortably | 145.97 tok/sEstimated | 3GB (have 48GB) |
| Qwen/Qwen2.5-3B-Instruct | FP16 | Fits comfortably | 74.61 tok/sEstimated | 6GB (have 48GB) |
| bigscience/bloomz-560m | Q4 | Fits comfortably | 172.35 tok/sEstimated | 4GB (have 48GB) |
| bigscience/bloomz-560m | Q8 | Fits comfortably | 133.25 tok/sEstimated | 7GB (have 48GB) |
| bigscience/bloomz-560m | FP16 | Fits comfortably | 68.45 tok/sEstimated | 15GB (have 48GB) |
| EssentialAI/rnj-1 | Q8 | Fits comfortably | 87.23 tok/sEstimated | 10GB (have 48GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Buy directly on Amazon with fast shipping and reliable customer service.
Rotate out primary variants whenever validation flags an issue.
Essential accessories to pair with NVIDIA RTX 6000 Ada
Total Bundle Price
All items from Amazon
💡 Not ready to buy? Try cloud GPUs first
Test NVIDIA RTX 6000 Ada performance in the cloud before investing in hardware. Pay by the hour with no commitment.
Data-backed answers pulled from community benchmarks, manufacturer specs, and live pricing.
LM Studio users fully offloading Qwen 3 30B Q4 with FlashAttention report about 33 tokens/sec at a 32K context window on the RTX 6000 Ada.
Source: Reddit – /r/LocalLLaMA (mpya1gb)
Professionals cite turnkey RTX 6000 Ada boxes at roughly $6,000—already fast and private enough to replace API workflows for many coding teams.
Source: Reddit – /r/LocalLLaMA (mr6x6wu)
One ProLiant DL380 Gen10 setup pairs a single RTX 6000 Ada with three RTX 4090s, virtualized under Proxmox to expose 120 GB of total VRAM for AI workloads.
Source: Reddit – /r/LocalLLaMA (mqubm2s)
Some buyers note the RTX 6000 Ada’s price (~$7k) rivals three RTX 5090 cards, so the workstation route only makes sense when ECC VRAM and pro drivers are required.
Source: Reddit – /r/LocalLLaMA (mqsk1ah)
RTX 6000 Ada includes 48 GB GDDR6 ECC, a 300 W TDP, and PCIe 4.0 x16 connectivity. As of Nov 2025 it listed at around $7,199 on Amazon.
Explore how RTX 4090 stacks up for local inference workloads.
Explore how NVIDIA A6000 stacks up for local inference workloads.
Explore how NVIDIA L40 stacks up for local inference workloads.
Explore how NVIDIA A5000 stacks up for local inference workloads.
Explore how Apple M3 Max stacks up for local inference workloads.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
Side-by-side VRAM, throughput, efficiency, and pricing benchmarks for both GPUs.
RPG • 2020
RPG • 2023
Action RPG • 2023
RPG • 2023
Survival Horror • 2023
Action RPG • 2022
Action RPG • 2024
Action Adventure • 2025
Survival Horror • 2023
Action • 2022
Action Adventure • 2023
Action Adventure • 2019