Best GPU for local AI

Which AI models can the Radeon RX 6800 XT run?

The Radeon RX 6800 XT has 16 GB of graphics memory, read at 512 GB/s. In a desktop with 32 GB of DDR4-3600, 44 of the 88 open models here run entirely on the card, 44 of them writing faster than you read; the most capable it runs well is Qwen3 14B at Q6_K, 23–38 tokens/s. 20 more run with part of the model in system memory, more slowly.

Every figure is for an 8,192-token conversation (about 6,000 words), at the best compression that fits. Try it in the calculator with your own memory, conversation length and compression, or a second card. For image and video models, see Stable Diffusion, FLUX and Wan on the Radeon RX 6800 XT.

Models that run entirely on the card

The fastest way to run a model: all of it in graphics memory. Most people read at 5 to 8 tokens a second, so anything above 10 feels quick.

Models the Radeon RX 6800 XT runs entirely on the card
ModelCompressionWritesNeeds
Qwen3 32BQ2_K22–36 tokens/s14 GB
Muse Glimmer 30BQ3_K_M20–33 tokens/s15 GB
Qwen3.6 27BQ3_K_M22–35 tokens/s14 GB
Qwen3.8 27BQ3_K_M22–35 tokens/s14 GB
Gemma 3 27BQ3_K_M21–34 tokens/s15 GB
Gemma 4 26B-A4BQ3_K_M96–155 tokens/s13 GB
Mistral Small 3.2 24BQ3_K_M24–39 tokens/s13 GB
Devstral Small 2 24BQ3_K_M24–39 tokens/s13 GB
LFM2.5 24B-A2BQ4_K_M115–185 tokens/s15 GB
gpt-oss 20BMXFP494–150 tokens/s12 GB
Qwen3 14BQ6_K23–38 tokens/s13 GB
Phi-4Q6_K23–37 tokens/s14 GB
Ministral 3 14BQ6_K25–40 tokens/s13 GB
Gemma 3 12BQ8_022–35 tokens/s14 GB
Gemma 4 12BQ8_023–37 tokens/s13 GB
Qwen3.5 9BQ8_029–46 tokens/s11 GB
Ornith 1.5 9BQ8_029–46 tokens/s11 GB
Ministral 3 8BQ8_030–48 tokens/s11 GB
Granite 4.2 8BQ8_030–48 tokens/s11 GB
Qwen3 8BQ8_032–51 tokens/s9.9 GB
DeepSeek R1 0528 Qwen3 8BQ8_032–51 tokens/s9.9 GB
Llama 3.1 8BQ8_033–53 tokens/s9.6 GB
Gemma 4 E4BQ8_060–96 tokens/s8.7 GB
Qwen2.5 7BQ8_036–57 tokens/s8.6 GB
Mistral 7B v0.3Q8_036–58 tokens/s8.8 GB
Gemma 4 E2BQ8_0110–180 tokens/s5.5 GB
Qwen3.5 4BQ8_057–92 tokens/s5.2 GB
Gemma 3 4BQ8_061–97 tokens/s4.9 GB
Qwen3 4B 2507Q8_060–96 tokens/s5.5 GB
Ministral 3 3BQ8_064–105 tokens/s5.0 GB
Phi-4-miniQ8_063–100 tokens/s5.2 GB
Granite 4.2 3BQ8_068–110 tokens/s4.6 GB
Llama 3.2 3BQ8_074–120 tokens/s4.4 GB
SmolLM3 3BQ8_080–130 tokens/s3.9 GB
Qwen3.5 2BQ8_0115–180 tokens/s2.5 GB
Qwen3 1.7BQ8_0105–170 tokens/s3.1 GB
LFM3 1.7BQ8_0145–230 tokens/s1.9 GB
Llama 3.2 1BQ8_0180–290 tokens/s1.6 GB
Gemma 3 1BQ8_0230–365 tokens/s1.1 GB
Qwen3.5 0.8BQ8_0250–400 tokens/s1.0 GB
Qwen3 0.6BQ8_0200–325 tokens/s1.7 GB
SmolLM2 360MQ8_0385–615 tokens/s0.7 GB
LFM2.5 350MQ8_0460–735 tokens/s0.5 GB
Gemma 3 270MQ8_0555–885 tokens/s0.3 GB

With help from system memory

In a desktop with 32 GB of DDR4-3600, these fit with part of the model in system memory. The processor reads that part several times more slowly, so they write more slowly the more of them is off the card.

Models the Radeon RX 6800 XT runs with part in system memory
ModelOn the cardWritesNeeds
Step 3.5 Flash37%15–24 tokens/s40 GB
Qwen3.8 Flash Next41%29–46 tokens/s36 GB
Mistral Medium 3.5 128B39%1.1–1.8 tokens/s38 GB
Qwen3.5 122B-A10B43%14–23 tokens/s35 GB
Nemotron 3.5 Super 120B-A12B43%12–20 tokens/s34 GB
Mistral Small 4 119B45%22–35 tokens/s33 GB
GLM-4.5 Air 106B-A12B46%11–17 tokens/s32 GB
Llama 4 Scout 109B-A17B47%8.1–13 tokens/s31 GB
Qwen3 Coder Next 80B-A3B38%23–37 tokens/s39 GB
Nemotron 3.5 Bolt 74B-A9B40%9.0–14 tokens/s37 GB
Qwen2.5 72B39%1.1–1.7 tokens/s38 GB
Llama 3.3 70B40%1.1–1.8 tokens/s37 GB
Flint 40B-A2.5B61%31–50 tokens/s24 GB
Qwen3.6 35B-A3B67%32–51 tokens/s22 GB
Nemotron 3.5 Lightning 30B-A3B77%41–66 tokens/s19 GB
Gemma 4 31B90%8.9–14 tokens/s16 GB
GLM-4.7 Flash76%37–59 tokens/s19 GB
Qwen3 30B-A3B 250777%32–51 tokens/s19 GB
Qwen3 Coder 30B-A3B77%32–51 tokens/s19 GB
Granite 4.2 30B90%9.4–15 tokens/s16 GB

The other 24 need more memory than a desktop with 32 GB of DDR4-3600 has; the calculator shows what more memory, or a second card, would add.

Two Radeon RX 6800 XTs

Two of them pool 32 GB, enough for 55 of the models entirely on the cards, 11 more than one holds. The most capable that runs well is Qwen3 32B at Q6_K, 11–17 tokens/s. A second card adds memory, not speed: a model that already fits on one runs no faster on two.

Compared with similar graphics

Tokens per second writing Llama 3.1 8B at 4 bits, which every 8 GB card holds, and the most capable model each runs well on its own. Every card ranked.

GraphicsMemoryLlama 3.1 8BMost capable it runs well
Radeon RX 680016 GB73Qwen3 14B
Radeon VII16 GB73Qwen3 14B
Instinct MI6032 GB72Qwen3 32B
Radeon RX 6800 XT16 GB72Qwen3 14B
RTX 3070 Ti8 GB72Qwen3.5 9B
RTX A450020 GB70Muse Glimmer 30B
RTX 2080 SUPER8 GB68Qwen3.5 9B

Estimated tokens per second; a real figure is usually within a quarter either way. * Part of it in system memory. {CAPABLE_NOTE}

How it is worked out

Writing an answer is limited by memory rather than arithmetic: for every word-piece (token) a model writes, the computer reads every weight that token uses once. So the speed is how fast the memory holding the model can be read, divided by how much is read for each token: the model’s weights at the chosen compression, plus the conversation so far. How much of its memory speed each graphics card reaches is measured, not assumed: it comes from the card’s own result in llama.cpp’s benchmark, which LM Studio, Ollama and Jan are built on. A model that does not fit in the graphics card’s memory spills into system memory, five to fifteen times slower, and one that does not fit in memory at all can be read from the drive as it runs, slower again; the estimate counts each part at its own speed, and a processor with few threads cannot use all of fast memory. Graphics built into a processor use system memory, at its speed. Reading a prompt is limited by arithmetic, so it follows the measured prompt speed.

Already running a model? See if it runs at full speed

A model that is slower than it should be is nearly always partly on the processor rather than all on the graphics card, which is several times slower — and Ollama does that without a word when something else holds the card’s memory. Bottleneck’s free app checks a model run with Ollama, LM Studio, llama.cpp, KoboldCpp or Jan: where it really runs, how fast it writes, and what your PC should manage with it. Nothing leaves your PC. Or check and fix it by hand.

It reads your parts too: the graphics card and how much memory it really has free, how much system memory is installed, the speed it runs at and in how many channels. Memory left in one channel, or below its rated speed, halves how fast a model runs from it, and Bottleneck says so. Its AI models screen then lists what runs on this PC.

The free version shows where a model runs and how fast, with no time limit and no account. Pro, $9 once, explains why it is slower than it could be and how to fix it. Compare the versions