Quick Answer: NVIDIA RTX 6000 Ada offers 48GB VRAM and starts around $6765.41. It delivers approximately 234 tokens/sec on google/gemma-3-1b-it. 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.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| google/gemma-3-1b-it | Q4 | 234.08 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 232.85 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 231.72 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 231.23 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 228.10 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 226.04 tok/sEstimated Auto-generated benchmark | 2GB |
| allenai/OLMo-2-0425-1B | Q4 | 223.85 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 223.48 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 221.72 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 220.28 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 220.11 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 218.15 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 217.30 tok/sEstimated Auto-generated benchmark | 1GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 216.79 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 213.28 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 212.74 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 212.41 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 210.47 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 208.77 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 206.90 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 204.61 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 204.30 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 204.01 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 203.98 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 200.00 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 195.58 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B | Q4 | 195.43 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 195.39 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 195.16 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-7B | Q4 | 194.87 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3-0324 | Q4 | 194.84 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2 | Q4 | 194.78 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | 194.63 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 194.48 tok/sEstimated Auto-generated benchmark | 2GB |
| zai-org/GLM-4.6-FP8 | Q4 | 194.39 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-3-270m-it | Q4 | 194.35 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/Phi-4-multimodal-instruct | Q4 | 194.30 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | 193.78 tok/sEstimated Auto-generated benchmark | 4GB |
| inference-net/Schematron-3B | Q4 | 193.57 tok/sEstimated Auto-generated benchmark | 2GB |
| rednote-hilab/dots.ocr | Q4 | 193.57 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 193.43 tok/sEstimated Auto-generated benchmark | 4GB |
| lmstudio-community/DeepSeek-R1-0528-Qwen3-8B-MLX-8bit | Q4 | 193.24 tok/sEstimated Auto-generated benchmark | 4GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 193.15 tok/sEstimated Auto-generated benchmark | 2GB |
| liuhaotian/llava-v1.5-7b | Q4 | 193.09 tok/sEstimated Auto-generated benchmark | 4GB |
| google-bert/bert-base-uncased | Q4 | 192.85 tok/sEstimated Auto-generated benchmark | 1GB |
| mistralai/Mistral-7B-v0.1 | Q4 | 192.49 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2-0.5B | Q4 | 192.41 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-8B-FP8 | Q4 | 191.83 tok/sEstimated Auto-generated benchmark | 4GB |
| HuggingFaceTB/SmolLM-135M | Q4 | 191.47 tok/sEstimated Auto-generated benchmark | 4GB |
| openai-community/gpt2-medium | Q4 | 190.97 tok/sEstimated Auto-generated benchmark | 4GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| meta-llama/Meta-Llama-3-8B | FP16 | Fits comfortably | 62.30 tok/sEstimated | 17GB (have 48GB) |
| Qwen/Qwen2.5-7B | Q4 | Fits comfortably | 194.87 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen2.5-7B | Q8 | Fits comfortably | 112.77 tok/sEstimated | 7GB (have 48GB) |
| Qwen/Qwen3-32B | Q4 | Fits comfortably | 58.28 tok/sEstimated | 16GB (have 48GB) |
| Qwen/Qwen3-32B | Q8 | Fits comfortably | 44.99 tok/sEstimated | 33GB (have 48GB) |
| Qwen/Qwen3-32B | FP16 | Not supported | 22.63 tok/sEstimated | 66GB (have 48GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct | Q4 | Fits comfortably | 35.40 tok/sEstimated | 39GB (have 48GB) |
| Qwen/Qwen3-Reranker-0.6B | Q4 | Fits comfortably | 166.95 tok/sEstimated | 3GB (have 48GB) |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q8 | Fits comfortably | 118.00 tok/sEstimated | 5GB (have 48GB) |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | FP16 | Fits comfortably | 63.44 tok/sEstimated | 9GB (have 48GB) |
| Qwen/Qwen2.5-1.5B | Q4 | Fits comfortably | 161.12 tok/sEstimated | 3GB (have 48GB) |
| zai-org/GLM-4.6-FP8 | Q4 | Fits comfortably | 194.39 tok/sEstimated | 4GB (have 48GB) |
| microsoft/DialoGPT-medium | Q4 | Fits comfortably | 190.05 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen3-0.6B-Base | Q8 | Fits comfortably | 117.19 tok/sEstimated | 6GB (have 48GB) |
| Qwen/Qwen3-0.6B-Base | FP16 | Fits comfortably | 74.49 tok/sEstimated | 13GB (have 48GB) |
| Qwen/Qwen3-8B-Base | Q4 | Fits comfortably | 164.25 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen3-8B-Base | Q8 | Fits comfortably | 124.91 tok/sEstimated | 9GB (have 48GB) |
| Qwen/Qwen3-Coder-30B-A3B-Instruct | Q8 | Fits comfortably | 71.96 tok/sEstimated | 31GB (have 48GB) |
| IlyaGusev/saiga_llama3_8b | Q8 | Fits comfortably | 125.38 tok/sEstimated | 9GB (have 48GB) |
| IlyaGusev/saiga_llama3_8b | FP16 | Fits comfortably | 65.51 tok/sEstimated | 17GB (have 48GB) |
| Qwen/Qwen2.5-Coder-1.5B | FP16 | Fits comfortably | 71.87 tok/sEstimated | 11GB (have 48GB) |
| rinna/japanese-gpt-neox-small | Q4 | Fits comfortably | 186.96 tok/sEstimated | 4GB (have 48GB) |
| rinna/japanese-gpt-neox-small | Q8 | Fits comfortably | 116.45 tok/sEstimated | 7GB (have 48GB) |
| sshleifer/tiny-gpt2 | Q4 | Fits comfortably | 180.15 tok/sEstimated | 4GB (have 48GB) |
| hmellor/tiny-random-LlamaForCausalLM | FP16 | Fits comfortably | 68.75 tok/sEstimated | 15GB (have 48GB) |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q4 | Fits comfortably | 193.78 tok/sEstimated | 4GB (have 48GB) |
| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | Q8 | Fits comfortably | 122.94 tok/sEstimated | 7GB (have 48GB) |
| Qwen/Qwen2.5-Coder-7B-Instruct | Q4 | Fits comfortably | 186.53 tok/sEstimated | 4GB (have 48GB) |
| microsoft/Phi-4-mini-instruct | Q8 | Fits comfortably | 124.65 tok/sEstimated | 7GB (have 48GB) |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | Fits comfortably | 163.61 tok/sEstimated | 4GB (have 48GB) |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q8 | Fits comfortably | 124.64 tok/sEstimated | 7GB (have 48GB) |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | FP16 | Fits comfortably | 65.70 tok/sEstimated | 15GB (have 48GB) |
| google/gemma-2b | Q4 | Fits comfortably | 217.30 tok/sEstimated | 1GB (have 48GB) |
| google/gemma-2b | FP16 | Fits comfortably | 88.52 tok/sEstimated | 4GB (have 48GB) |
| liuhaotian/llava-v1.5-7b | Q4 | Fits comfortably | 193.09 tok/sEstimated | 4GB (have 48GB) |
| liuhaotian/llava-v1.5-7b | Q8 | Fits comfortably | 124.20 tok/sEstimated | 7GB (have 48GB) |
| nvidia/NVIDIA-Nemotron-Nano-9B-v2 | Q8 | Fits comfortably | 95.14 tok/sEstimated | 10GB (have 48GB) |
| nvidia/NVIDIA-Nemotron-Nano-9B-v2 | FP16 | Fits comfortably | 50.62 tok/sEstimated | 19GB (have 48GB) |
| vikhyatk/moondream2 | Q8 | Fits comfortably | 132.10 tok/sEstimated | 7GB (have 48GB) |
| vikhyatk/moondream2 | FP16 | Fits comfortably | 73.73 tok/sEstimated | 15GB (have 48GB) |
| petals-team/StableBeluga2 | Q4 | Fits comfortably | 180.91 tok/sEstimated | 4GB (have 48GB) |
| petals-team/StableBeluga2 | Q8 | Fits comfortably | 136.70 tok/sEstimated | 7GB (have 48GB) |
| petals-team/StableBeluga2 | FP16 | Fits comfortably | 66.58 tok/sEstimated | 15GB (have 48GB) |
| NousResearch/Meta-Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 171.01 tok/sEstimated | 4GB (have 48GB) |
| Qwen/Qwen3-30B-A3B-Thinking-2507 | Q4 | Fits comfortably | 90.76 tok/sEstimated | 15GB (have 48GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q4 | Fits comfortably | 103.32 tok/sEstimated | 15GB (have 48GB) |
| lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-5bit | Q8 | Fits comfortably | 68.55 tok/sEstimated | 31GB (have 48GB) |
| deepseek-ai/DeepSeek-V3 | Q8 | Fits comfortably | 130.70 tok/sEstimated | 7GB (have 48GB) |
| deepseek-ai/DeepSeek-V3 | FP16 | Fits comfortably | 67.20 tok/sEstimated | 15GB (have 48GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q8 | Not supported | 47.54 tok/sEstimated | 68GB (have 48GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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.
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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.