Quick Answer: RX 6900 XT offers 16GB VRAM and starts around current market pricing. It delivers approximately 109 tokens/sec on meta-llama/Llama-3.2-1B-Instruct. 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 |
|---|---|---|---|
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 109.00 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 108.96 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 108.77 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/gemma-3-1b-it | Q4 | 108.16 tok/sEstimated Auto-generated benchmark | 1GB |
| bigcode/starcoder2-3b | Q4 | 107.89 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 106.71 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 106.23 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 104.92 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-1B | Q4 | 103.83 tok/sEstimated Auto-generated benchmark | 1GB |
| google/embeddinggemma-300m | Q4 | 103.74 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 103.33 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-2-2b-it | Q4 | 101.59 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 101.33 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 101.15 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 100.69 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 100.48 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2.5-3B | Q4 | 100.41 tok/sEstimated Auto-generated benchmark | 2GB |
| nari-labs/Dia2-2B | Q4 | 99.11 tok/sEstimated Auto-generated benchmark | 2GB |
| google-bert/bert-base-uncased | Q4 | 97.43 tok/sEstimated Auto-generated benchmark | 1GB |
| meta-llama/Llama-3.2-3B | Q4 | 96.97 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 96.79 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 96.38 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 95.46 tok/sEstimated Auto-generated benchmark | 2GB |
| ibm-research/PowerMoE-3b | Q4 | 95.38 tok/sEstimated Auto-generated benchmark | 2GB |
| tencent/HunyuanOCR | Q4 | 93.42 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-3-1b-it | Q4 | 91.38 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 90.94 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-1.5B | Q4 | 90.78 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-4B-Base | Q4 | 90.65 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 90.40 tok/sEstimated Auto-generated benchmark | 2GB |
| BSC-LT/salamandraTA-7b-instruct | Q4 | 90.40 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 90.35 tok/sEstimated Auto-generated benchmark | 3GB |
| meta-llama/Llama-2-7b-chat-hf | Q4 | 90.30 tok/sEstimated Auto-generated benchmark | 4GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 90.30 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/VibeVoice-1.5B | Q4 | 90.28 tok/sEstimated Auto-generated benchmark | 3GB |
| black-forest-labs/FLUX.1-dev | Q4 | 90.20 tok/sEstimated Auto-generated benchmark | 4GB |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q4 | 89.95 tok/sEstimated Auto-generated benchmark | 2GB |
| microsoft/Phi-3.5-mini-instruct | Q4 | 89.81 tok/sEstimated Auto-generated benchmark | 4GB |
| inference-net/Schematron-3B | Q4 | 89.81 tok/sEstimated Auto-generated benchmark | 2GB |
| black-forest-labs/FLUX.2-dev | Q4 | 89.72 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 89.71 tok/sEstimated Auto-generated benchmark | 1GB |
| facebook/sam3 | Q4 | 89.68 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-8B-FP8 | Q4 | 89.54 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-7B-Instruct | Q4 | 89.42 tok/sEstimated Auto-generated benchmark | 4GB |
| bigscience/bloomz-560m | Q4 | 89.39 tok/sEstimated Auto-generated benchmark | 4GB |
| google/gemma-3-270m-it | Q4 | 89.30 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-R1-0528 | Q4 | 89.09 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 89.01 tok/sEstimated Auto-generated benchmark | 4GB |
| microsoft/phi-2 | Q4 | 88.84 tok/sEstimated Auto-generated benchmark | 4GB |
| EleutherAI/pythia-70m-deduped | Q4 | 88.79 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 |
|---|---|---|---|---|
| Gensyn/Qwen2.5-0.5B-Instruct | FP16 | Fits comfortably | 33.60 tok/sEstimated | 11GB (have 16GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q4 | Fits comfortably | 85.83 tok/sEstimated | 4GB (have 16GB) |
| meta-llama/Llama-3.1-8B-Instruct | Q8 | Fits comfortably | 56.91 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.1-8B-Instruct | FP16 | Not supported | 31.11 tok/sEstimated | 17GB (have 16GB) |
| openai/gpt-oss-20b | Q4 | Fits comfortably | 41.30 tok/sEstimated | 10GB (have 16GB) |
| openai/gpt-oss-20b | Q8 | Not supported | 32.62 tok/sEstimated | 20GB (have 16GB) |
| openai/gpt-oss-20b | FP16 | Not supported | 18.72 tok/sEstimated | 41GB (have 16GB) |
| google/gemma-3-1b-it | Q4 | Fits comfortably | 91.38 tok/sEstimated | 1GB (have 16GB) |
| Qwen/Qwen3-Embedding-0.6B | FP16 | Fits comfortably | 32.43 tok/sEstimated | 13GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q4 | Fits comfortably | 79.99 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | Q8 | Fits comfortably | 61.02 tok/sEstimated | 5GB (have 16GB) |
| Qwen/Qwen2.5-1.5B-Instruct | FP16 | Fits comfortably | 28.48 tok/sEstimated | 11GB (have 16GB) |
| facebook/opt-125m | Q4 | Fits comfortably | 76.26 tok/sEstimated | 4GB (have 16GB) |
| facebook/opt-125m | Q8 | Fits comfortably | 63.22 tok/sEstimated | 7GB (have 16GB) |
| facebook/opt-125m | FP16 | Fits (tight) | 33.95 tok/sEstimated | 15GB (have 16GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q8 | Fits comfortably | 71.37 tok/sEstimated | 1GB (have 16GB) |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | FP16 | Fits comfortably | 39.99 tok/sEstimated | 2GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q4 | Fits comfortably | 75.37 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen2.5-3B-Instruct | FP16 | Fits comfortably | 35.93 tok/sEstimated | 6GB (have 16GB) |
| openai-community/gpt2 | Q4 | Fits comfortably | 77.69 tok/sEstimated | 4GB (have 16GB) |
| openai-community/gpt2 | Q8 | Fits comfortably | 62.81 tok/sEstimated | 7GB (have 16GB) |
| openai-community/gpt2 | FP16 | Fits (tight) | 30.63 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen2.5-7B-Instruct | Q4 | Fits comfortably | 84.87 tok/sEstimated | 4GB (have 16GB) |
| google/gemma-3-1b-it | Q8 | Fits comfortably | 68.97 tok/sEstimated | 1GB (have 16GB) |
| google/gemma-3-1b-it | FP16 | Fits comfortably | 38.43 tok/sEstimated | 2GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | Q8 | Fits comfortably | 57.23 tok/sEstimated | 7GB (have 16GB) |
| trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | FP16 | Fits (tight) | 32.51 tok/sEstimated | 15GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q4 | Fits comfortably | 77.51 tok/sEstimated | 2GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | Q8 | Fits comfortably | 54.48 tok/sEstimated | 4GB (have 16GB) |
| Qwen/Qwen3-4B-Instruct-2507 | FP16 | Fits comfortably | 28.51 tok/sEstimated | 9GB (have 16GB) |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | Fits comfortably | 109.00 tok/sEstimated | 1GB (have 16GB) |
| openai/gpt-oss-120b | FP16 | Not supported | 6.60 tok/sEstimated | 235GB (have 16GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | Q4 | Not supported | 9.17 tok/sEstimated | 378GB (have 16GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | Q8 | Not supported | 7.22 tok/sEstimated | 755GB (have 16GB) |
| mistralai/Mistral-Large-3-675B-Instruct-2512 | FP16 | Not supported | 3.48 tok/sEstimated | 1509GB (have 16GB) |
| EssentialAI/rnj-1 | Q4 | Fits comfortably | 61.88 tok/sEstimated | 5GB (have 16GB) |
| EssentialAI/rnj-1 | Q8 | Fits comfortably | 44.08 tok/sEstimated | 10GB (have 16GB) |
| Qwen/Qwen2.5-3B-Instruct | Q4 | Fits comfortably | 100.69 tok/sEstimated | 2GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | Q8 | Not supported | 19.46 tok/sEstimated | 68GB (have 16GB) |
| RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | FP16 | Not supported | 11.44 tok/sEstimated | 137GB (have 16GB) |
| EssentialAI/rnj-1 | FP16 | Not supported | 21.45 tok/sEstimated | 19GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | Fits comfortably | 100.48 tok/sEstimated | 2GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | Q8 | Fits comfortably | 66.94 tok/sEstimated | 3GB (have 16GB) |
| Qwen/Qwen3-32B | Q4 | Fits (tight) | 28.18 tok/sEstimated | 16GB (have 16GB) |
| meta-llama/Llama-3.2-3B-Instruct | FP16 | Fits comfortably | 36.79 tok/sEstimated | 6GB (have 16GB) |
| vikhyatk/moondream2 | FP16 | Fits (tight) | 34.24 tok/sEstimated | 15GB (have 16GB) |
| meta-llama/Llama-3.2-1B | Q4 | Fits comfortably | 103.83 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B | Q8 | Fits comfortably | 67.88 tok/sEstimated | 1GB (have 16GB) |
| meta-llama/Llama-3.2-1B | FP16 | Fits comfortably | 36.51 tok/sEstimated | 2GB (have 16GB) |
| Gensyn/Qwen2.5-0.5B-Instruct | Q8 | Fits comfortably | 60.59 tok/sEstimated | 5GB (have 16GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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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.