Best Local AI Models for AMD Radeon RX 7900 XT (20 GB VRAM)

Q: What is the VRAM and memory bandwidth of the AMD Radeon RX 7900 XT?

The AMD Radeon RX 7900 XT features 20 GB of VRAM and a memory bandwidth of 800 GB/s (GDDR6 320-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

Q: What is the best local AI model to run on a AMD Radeon RX 7900 XT?

The best overall model for the AMD Radeon RX 7900 XT is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 20 GB of VRAM, this GPU typically runs a 14B at high quality, or a 24–27B at 4-bit at full GPU speed. Check our guide to the best LLMs for 16 GB VRAM for details.

The AMD Radeon RX 7900 XT has 20 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~32.0 GB system RAM assumed), ranked by popularity.

Verdict: Generous 20 GB VRAM buffer: comfortably runs 14B at uncompressed Q8 and 32B at Q4.

VRAM & Memory

20 GB

GDDR6 320-bit

Bandwidth

800 GB/s

RDNA 3

Fits in VRAM

36 models

Zero offload

TDP / Assumed RAM

315W

RAM: 32.0 GB

At a glance — 36 of the 38 most popular models run fully in VRAM on the AMD Radeon RX 7900 XT (20 GB), with about 133 tokens/s estimated on an 8B model in Q4. A solid pick is Qwen/Qwen3-8B-GGUF (Q8_0, 9.47 GB, ~98.6 t/s estimated).

The AMD Radeon RX 7900 XT is built on AMD's RDNA 3 architecture featuring GDDR6 320-bit delivering 800 GB/s of raw memory bandwidth. Generous 20 GB VRAM buffer: comfortably runs 14B at uncompressed Q8 and 32B at Q4.

Equipped with 20 GB of dedicated VRAM, the AMD Radeon RX 7900 XT can run 36 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-8B-GGUF, Qwen3-4B-GGUF, Qwen2.5-7B-Instruct-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 16 GB VRAM.

With a memory bandwidth of 800 GB/s, this card can generate tokens at an estimated peak rate of ~133.3 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 315W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.

New to this? Read: How much VRAM do you need?

How the AMD Radeon RX 7900 XT compares with similar cards

Compared with the AMD Radeon RX 7900 XTX, the AMD Radeon RX 7900 XT has 4 GB less VRAM (20 vs 24 GB), so fewer models fit fully on the GPU; about 17% slower token generation (~133 vs ~160 tokens/s on an 8B model in Q4, from 800 vs 960 GB/s) and a lower board power (315 vs 355 W). Side-by-side comparison
Compared with the AMD Radeon RX 7800 XT, the AMD Radeon RX 7900 XT has 4 GB more VRAM (20 vs 16 GB), so larger models or longer contexts fit; about 28% faster token generation (~133 vs ~104 tokens/s on an 8B model in Q4, from 800 vs 624 GB/s) and a higher board power (315 vs 263 W). Side-by-side comparison
Compared with the AMD Radeon RX 6950 XT, the AMD Radeon RX 7900 XT has 4 GB more VRAM (20 vs 16 GB), so larger models or longer contexts fit; about 39% faster token generation (~133 vs ~96 tokens/s on an 8B model in Q4, from 800 vs 576 GB/s); a lower board power (315 vs 335 W) and a newer architecture (RDNA 3 vs RDNA 2). Side-by-side comparison

Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.

36 fit fully in VRAM · 2 run with offload

Only show models that fit fully in VRAM

Model	Size	Quant.	Quality	Memory	Speed~	Verdict
Qwen/Qwen3-8B-GGUF	8.19B	Q8_0	Excellent	9.47 GB	98.6 t/s	Fits in VRAM
Qwen/Qwen3-4B-GGUF	4.02B	Q8_0	Excellent	5.35 GB	200.7 t/s	Fits in VRAM
Qwen/Qwen2.5-7B-Instruct-GGUF	7.62B	GGUF	Excellent	15.21 GB	56.4 t/s	Fits in VRAM
unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF	8.0B	BF16	Excellent	14.87 GB	57.1 t/s	Fits in VRAM
unsloth/gpt-oss-20b-GGUF	20.91B	F16	Very good	13.74 GB	62.3 t/s	Fits in VRAM
bartowski/Qwen2.5-32B-Instruct-GGUF	32.76B	Q4_K_S	Good	19.29 GB	45.7 t/s	Fits in VRAM
Qwen/Qwen2.5-1.5B-Instruct-GGUF	1.54B	GGUF	Excellent	4.23 GB	241.3 t/s	Fits in VRAM
unsloth/LFM2.5-24B-A2B-GGUF	23.97B	Q5_K_XL	Very good	17.57 GB	47.9 t/s	Fits in VRAM
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF	30.53B	Q4_1	Very good	19.05 GB	44.8 t/s	Fits in VRAM
Qwen/Qwen2.5-0.5B-Instruct-GGUF	0.49B	GGUF	Excellent	2.03 GB	678.3 t/s	Fits in VRAM
unsloth/Ornith-1.0-9B-GGUF	—	BF16	Excellent	17.61 GB	47.9 t/s	Fits in VRAM
Qwen/Qwen3-14B-GGUF	14.77B	Q8_0	Excellent	16.05 GB	54.7 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF	7.62B	GGUF	Excellent	15.21 GB	56.4 t/s	Fits in VRAM
ggml-org/gpt-oss-120b-GGUF	116.83B	BF16	Very low	2.42 GB	541.3 t/s	Fits in VRAM
LiquidAI/LFM3-1.7B-GGUF	1.73B	BF16	Excellent	4.08 GB	247.8 t/s	Fits in VRAM
unsloth/flint-GGUF	39.79B	Q3_K_XL	Fair	18.13 GB	46.4 t/s	Fits in VRAM
janhq/Jan-v3.5-4B-gguf	4.41B	GGUF	Excellent	9.59 GB	97.3 t/s	Fits in VRAM
Qwen/Qwen2.5-3B-Instruct-GGUF	3.09B	GGUF	Excellent	7.27 GB	126.3 t/s	Fits in VRAM
unsloth/GLM-4.7-Flash-GGUF	31.22B	Q4_1	Good	18.68 GB	45.3 t/s	Fits in VRAM
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF	8.03B	Q8_0	Excellent	9.23 GB	100.6 t/s	Fits in VRAM
unsloth/Llama-3.2-3B-Instruct-GGUF	3.21B	F16	Excellent	7.23 GB	133.5 t/s	Fits in VRAM
LiquidAI/LFM2.5-1.2B-Instruct-GGUF	1.17B	BF16	Excellent	3.03 GB	366.6 t/s	Fits in VRAM
lmstudio-community/gemma-3-1b-it-GGUF	1.0B	Q8_0	Excellent	1.96 GB	803.3 t/s	Fits in VRAM
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF	34.66B	Q4_0	Good	19.45 GB	43.1 t/s	Fits in VRAM
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	Q4_0	Good	18.42 GB	45.5 t/s	Fits in VRAM
Qwen/Qwen3-0.6B-GGUF	0.75B	Q8_0	Excellent	1.83 GB	1343.3 t/s	Fits in VRAM
bartowski/Qwen_Qwen3-0.6B-GGUF	0.75B	BF16	Excellent	2.64 GB	569.1 t/s	Fits in VRAM
bartowski/Llama-3.3-70B-Instruct-GGUF	70.55B	IQ1_M	Very low	17.82 GB	51.3 t/s	Fits in VRAM
unsloth/Qwen3-1.7B-GGUF	1.72B	BF16	Excellent	4.45 GB	249.2 t/s	Fits in VRAM
LiquidAI/LFM2.5-350M-GGUF	0.35B	BF16	Excellent	1.51 GB	1207.3 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF	14.77B	Q8_0	Excellent	16.17 GB	54.7 t/s	Fits in VRAM
unsloth/Qwen3-Coder-Next-GGUF	79.67B	TQ1_0	Very low	18.82 GB	45.3 t/s	Fits in VRAM
unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF	8.19B	BF16	Excellent	16.63 GB	52.4 t/s	Fits in VRAM
bartowski/Nanbeige_Nanbeige4.3-33B-GGUF	33.12B	Q4_K_S	Good	20.0 GB	43.5 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	453.4 t/s	Fits in VRAM
bartowski/Qwen2.5-14B-Instruct-GGUF	14.77B	Q8_0	Excellent	16.17 GB	54.7 t/s	Fits in VRAM
unsloth/Ornith-1.5-122B-GGUF	125.09B	IQ3_XXS	Low	47.01 GB	1.1 t/s	Offload
google/gemma-7b	8.54B	GGUF	Excellent	33.11 GB	1.6 t/s	Offload

"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the AMD Radeon RX 7900 XT's 800 GB/s memory bandwidth (offloaded models are limited by system RAM).

Frequently asked questions

What is the VRAM and memory bandwidth of the AMD Radeon RX 7900 XT?

The AMD Radeon RX 7900 XT features 20 GB of VRAM and a memory bandwidth of 800 GB/s (GDDR6 320-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

What is the best local AI model to run on a AMD Radeon RX 7900 XT?

The best overall model for the AMD Radeon RX 7900 XT is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 20 GB of VRAM, this GPU typically runs a 14B at high quality, or a 24–27B at 4-bit at full GPU speed. Check our guide to the best LLMs for 16 GB VRAM for details.

Can the AMD Radeon RX 7900 XT run 14B and 32B models?

Yes. A 16 GB VRAM buffer allows you to run 14B models (like Qwen 2.5 14B or DeepSeek-R1 14B) in high-fidelity Q8_0 quantizations, or 32B models (like Qwen 2.5 32B) in Q3_K_M or Q4_K_M quantizations fully on the GPU.

Does the AMD Radeon RX 7900 XT work with Ollama and LM Studio?

Yes. Under Linux, the AMD Radeon RX 7900 XT supports native ROCm acceleration in Ollama and llama.cpp. On Windows, Ollama and LM Studio utilize the Vulkan or DirectML backends to leverage the card's full 20 GB VRAM pool.