Desktop GPU · AMD

What AI models can a Radeon RX 6700 XT run?

12 GB is the point where local models stop being a demo. The popular 7B to 14B models fit with room for a real conversation, and a 32B fits if you accept a lower quantization. At 384 GB/s there is enough bandwidth to keep generation responsive for anything that fits.

Memory12 GB
Bandwidth384 GB/s
Usable for a model11.2 GB
Runtime backendROCM
ArchitectureRDNA 2
Power230 W

The short answer

Assuming an 8k context window and default settings, these are the models worth downloading first.

See how fast it feels

Radeon RX 6700 XT running Gemma 4 12B Instruct at Q6_K

Wait for the first word1.6 s
Then writes at22.9 tok/s
Whole answer9.2 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Where it sits

With 12 GB it belongs to the 12 GB tier. The 12 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 384 GB/s decides how fast it answers.

Our indicative price for this card is $250 used, reviewed 2026-09-01. That is 4.8 GB of memory and 154 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.

Devices in a similar price band, fastest memory first:

DevicePriceMemoryBandwidthBest all-round pick
GeForce RTX 2080 Ti $250 used 11 GB 616 GB/s Qwen3.5 9B, 51.3 t/s
GeForce RTX 3070 Ti $300 used 8 GB 608 GB/s Qwen3.5 9B, 74.4 t/s
Arc A770 16GB $280 used 16 GB 560 GB/s Qwen3.5 27B, 23.1 t/s
Arc A750 $200 used 8 GB 512 GB/s Qwen3.5 9B, 48.3 t/s

Every model, scored on this device

Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.

Model Params Verdict Download Memory used Speed Max context
Gemma 4 12B Instruct
Gemma
11.96B Just fits Q6_K 10.84 GB 22.9 t/s 8k
Qwen3.5 9B
Qwen
9.65B Runs great Q8_0 9.25 GB 27.1 t/s 64k
Qwen3 14B
Qwen
14.77B Just fits Q5_K_M 10.96 GB 22.8 t/s 8k
Gemma 3 12B Instruct
Gemma
12.19B Just fits Q6_K 10.52 GB 23.7 t/s 16k
Gemma 4 E4B Instruct
Gemma
8B Runs great Q8_0 8.42 GB 29.7 t/s 128k
Granite 4.1 8B
Granite
8.79B Just fits Q8_0 10.35 GB 24.1 t/s 8k
Qwen2.5 14B Instruct
Qwen
14.77B Runs great Q4_K_M 9.81 GB 25.6 t/s 8k
Qwen3 8B
Qwen
8.19B Runs great Q8_0 9.02 GB 27.8 t/s 16k
Qwen2.5 Coder 14B Instruct
Qwen
14.77B Runs great Q4_K_M 9.81 GB 25.6 t/s 8k
Qwen2.5 VL 7B Instruct
Qwen
8.29B Runs great Q8_0 8.49 GB 29.6 t/s 32k
Phi 4
Phi
14.66B Runs great Q4_K_M 9.96 GB 25.2 t/s 8k
Mistral Nemo Instruct 2407
Mistral
12.25B Just fits Q6_K 10.53 GB 23.7 t/s 8k
Qwen3.5 27B
Qwen
27.78B Fits only at 2 to 3 bits IQ3_XXS ! 10.53 GB 23.8 t/s 16k
Qwen2.5 7B Instruct
Qwen
7.62B Runs great Q8_0 7.83 GB 32.2 t/s 32k
Llama 3.1 8B Instruct
Llama
8.03B Runs great Q8_0 8.83 GB 28.4 t/s 16k
Qwen2.5 Coder 7B Instruct
Qwen
7.62B Runs great Q8_0 7.83 GB 32.2 t/s 32k
Gemma 2 9B Instruct
Gemma
9.24B Runs great Q6_K 9.42 GB 26.5 t/s 8k
OLMo 3 7B Instruct
OLMo
7.3B Runs great Q8_0 9.72 GB 25.7 t/s 16k
Internlm3 8B Instruct
InternLM
8.8B Runs great Q8_0 8.97 GB 28 t/s 32k
Mistral 7B Instruct V0.3
Mistral
7.25B Runs great Q8_0 8.45 GB 29.8 t/s 16k
DeepSeek Coder V2 Lite Instruct
DeepSeek
15.71B (2.74B active) Just fits Q5_K_M 10.84 GB 68.5 t/s 16k
Gemma 4 E2B Instruct
Gemma
5.12B Runs great Q8_0 5.44 GB 46.8 t/s 128k
Qwen3.5 4B
Qwen
4.66B Runs great Q8_0 5.27 GB 48.8 t/s 128k
Mistral 7B Instruct V0.2
Mistral
7.24B Runs great Q8_0 8.44 GB 29.8 t/s 16k
DeepSeek Coder 7B Instruct V1.5
DeepSeek
6.91B Just fits Q8_0 10.58 GB 23.6 t/s 4k
Mistral Small 24B Instruct 2501
Mistral
23.57B Fits only at 2 to 3 bits IQ3_XXS ! 9.85 GB 25.5 t/s 16k
DeepSeek Coder 6.7B Instruct
DeepSeek
6.74B Just fits Q8_0 10.94 GB 22.7 t/s 8k
LFM2.5 8B A1B
Liquid
8.47B (1.57B active) Runs great Q8_0 8.81 GB 84.4 t/s 64k
Qwen3 4B
Qwen
4.02B Runs great Q8_0 5.46 GB 47 t/s 32k
Codestral 22B V0.1
Mistral
22.25B Fits only at 2 to 3 bits IQ3_XXS ! 10.08 GB 24.9 t/s 8k
Show the other 56 models
Gemma 3 4B Instruct 4.3B Runs great Q8_0 4.91 GB 52.6 t/s 128k
Phi 4 Mini Instruct 3.84B Runs great Q8_0 5.17 GB 49.9 t/s 32k
Granite 4.1 3B 3.4B Runs great Q8_0 4.34 GB 60.1 t/s 64k
Qwen3 32B 32.76B Fits only at 2 to 3 bits IQ2_XXS 10.11 GB 24.8 t/s 8k
Qwen2.5 3B Instruct 3.09B Runs great Q8_0 3.68 GB 71.8 t/s 32k
Phi 3 Mini 4k Instruct 3.82B Runs great Q8_0 4.8 GB 54.1 t/s 4k
Llama 3.2 3B Instruct 3.21B Runs great Q8_0 4.42 GB 59.1 t/s 64k
Granite 4.1 30B 28.87B Fits only at 2 to 3 bits IQ2_XXS 9.33 GB 26.9 t/s 8k
Qwen2.5 32B Instruct 32.76B Fits only at 2 to 3 bits IQ2_XXS 10.11 GB 24.8 t/s 8k
Qwen2.5 Coder 32B Instruct 32.76B Fits only at 2 to 3 bits IQ2_XXS 10.11 GB 24.8 t/s 8k
LFM2.5 2.6B 2.7B Runs great Q8_0 3.14 GB 85.7 t/s 128k
Qwen3.5 2B 2.27B Runs great Q8_0 2.7 GB 101.6 t/s 256k
Gemma 3 27B Instruct 27.43B Fits only at 2 to 3 bits IQ2_XXS 8.11 GB 31.3 t/s 32k
Gemma 2 2B Instruct 2.61B Runs great Q8_0 3.54 GB 75.1 t/s 8k
Starcoder2 3B 3.03B Runs great Q8_0 3.34 GB 80.8 t/s 16k
Qwen3 1.7B 2.03B Runs great Q8_0 3.22 GB 83.1 t/s 32k
DeepSeek R1 Distill Qwen 1.5B 1.78B Runs great Q8_0 2.07 GB 137 t/s 128k
Qwen2.5 1.5B Instruct 1.54B Runs great Q8_0 2.06 GB 137.5 t/s 32k
SmolLM2 1.7B 1.71B Runs great Q8_0 3.53 GB 75.1 t/s 8k
OLMo 2 0425 1B 1.48B Runs great Q8_0 2.6 GB 106 t/s 4k
Llama 3.2 1B Instruct 1.24B Runs great Q8_0 1.82 GB 162.2 t/s 128k
LFM2.5 1.2B Instruct 1.17B Runs great Q8_0 1.46 GB 214.2 t/s 64k
Gemma 3 1B Instruct 1B Runs great Q8_0 1.35 GB 230.7 t/s 32k
Qwen3.5 0.8B 0.87B Runs great Q8_0 1.28 GB 246.3 t/s 256k
Qwen3 0.6B 0.75B Runs great Q8_0 1.92 GB 148.2 t/s 32k
Qwen2.5 0.5B Instruct 0.49B Runs great Q8_0 0.88 GB 414.1 t/s 32k
LFM2.5 350M 0.35B Runs great Q8_0 0.68 GB 641.2 t/s 64k
GPT OSS 20B 20.91B Partial offload n/a 11.55 GB 59.9 t/s n/a
Qwen3.5 35B A3B 35.95B Partial offload n/a 20.48 GB 53.1 t/s n/a
GLM 4.7 Flash 31.22B Partial offload n/a 18.13 GB 35.9 t/s n/a
Gemma 4 26B A4B Instruct 25.81B Partial offload n/a 15.7 GB 35.2 t/s n/a
Qwen3 30B A3B 30.53B Partial offload n/a 18.08 GB 34.6 t/s n/a
Qwen3 Coder 30B A3B Instruct 30.53B Partial offload n/a 18.08 GB 34.6 t/s n/a
Qwen3 Coder Next 79.67B Partial offload n/a 45.22 GB 32.3 t/s n/a
Qwen3 Next 80B A3B Instruct 81.32B Partial offload n/a 46.15 GB 31.8 t/s n/a
GPT OSS 120B 116.83B Partial offload n/a 59.4 GB 16.6 t/s n/a
Phi 3.5 MoE Instruct 41.87B Partial offload n/a 24.87 GB 12.8 t/s n/a
Qwen3.5 122B A10B 125.09B Partial offload n/a 70.61 GB 12.3 t/s n/a
GLM 4.5 Air 110.47B Partial offload n/a 63.61 GB 6.8 t/s n/a
MiniMax M2.5 228.7B Partial offload n/a 130.56 GB 6.5 t/s n/a
MiniMax M2.7 228.69B Partial offload n/a 130.55 GB 6.5 t/s n/a
Mixtral 8x7B Instruct V0.1 46.7B Partial offload n/a 27.59 GB 6 t/s n/a
MiniMax M3 427.04B Partial offload n/a 240.84 GB 3.4 t/s n/a
Qwen3 235B A22B 235.09B Partial offload n/a 133.71 GB 3.4 t/s n/a
Gemma 4 31B Instruct 31.27B Partial offload n/a 21.31 GB 2.5 t/s n/a
DeepSeek V3.2 685.4B Partial offload n/a 385.91 GB 2.2 t/s n/a
DeepSeek R1 684.53B Partial offload n/a 385.42 GB 2.2 t/s n/a
Kimi K2.6 1026.88B Partial offload n/a 577.78 GB 2.1 t/s n/a
Kimi K2 Instruct 1026.41B Partial offload n/a 577.52 GB 2.1 t/s n/a
GLM 5.1 753.86B Partial offload n/a 424.51 GB 2.1 t/s n/a
GLM 4.5 358.34B Partial offload n/a 204.37 GB 1.4 t/s n/a
GLM 5.2 753.33B Partial offload n/a 424.21 GB 1 t/s n/a
Qwen2.5 VL 72B Instruct 73.41B Partial offload n/a 43.62 GB 0.9 t/s n/a
Qwen2.5 72B Instruct 72.71B Partial offload n/a 43.22 GB 0.9 t/s n/a
Llama 3.3 70B Instruct 70.55B Partial offload n/a 42.12 GB 0.9 t/s n/a
Llama 3 3 Nemotron Super 49B V1 49.87B Partial offload n/a 47.99 GB 0.8 t/s n/a

What it will not run

0 of the 86 architectures we track are out of reach here, even at 2-bit precision. Another 29 run by splitting the model between the card and system RAM, which works but drops generation to single digits for most dense models.

If you want more headroom

The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.

DeviceMemoryBandwidthModels that fit
GeForce RTX 5080 16 GB 960 GB/s 63 Check price
GeForce RTX 5070 Ti 16 GB 896 GB/s 63 Check price
GeForce RTX 4080 SUPER 16 GB 736 GB/s 63 Check price
GeForce RTX 4080 16 GB 717 GB/s 63 Check price

Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.

Other devices with 12 GB

Same capacity, different speed. Once a model fits, bandwidth is what separates these.

Questions people ask about an RX 6700 XT

How much VRAM does a Radeon RX 6700 XT have?

The Radeon RX 6700 XT has 12 GB of VRAM, of which roughly 11.2 GB is available to a model once the operating system takes its share.

Can a Radeon RX 6700 XT run a 70B model?

Only partially. About 48 of 48 layers fit on the card and the rest run on the CPU, which brings generation down to roughly 32.3 tokens per second.

What is the best local model for a Radeon RX 6700 XT?

Gemma 4 12B Instruct at Q6_K is the strongest all-round fit: 10.84 GB used, around 22.9 tokens per second at 8k context.