What LLMs Can the Radeon RX 7900 XTX 24GB Run?

The Radeon RX 7900 XTX 24GB has 24 GB of VRAM and 960 GB/s of bandwidth. It runs 41 of 58 models fully at Q4_K_M with 8K context, up to Qwen 3.5 35B-A3B, and generates about 95 tokens/s on an 8B model.

AMD · RDNA 3 · Released 2022-12 · Updated October 14, 2026

41 of 58 models fit

Radeon RX 7900 XTX 24GB (24.0 GB) at Q4_K_M with 8K context. Largest that fits fully: Qwen 3.5 35B-A3B.

ModelNeedsStatusSpeedBest quant that fits
Qwen 2.5 32B21.4 GBFits~25 tok/sQ4_K_M
Qwen 2.5 Coder 32B21.4 GBFits~25 tok/sQ4_K_M
DeepSeek R1 Distill Qwen 32B21.4 GBFits~25 tok/sQ4_K_M
Qwen 3 32B21.4 GBFits~25 tok/sQ4_K_M
Qwen 3.5 35B-A3B21.0 GBFits~129 tok/sQ4_K_M
Qwen 3.6 35B-A3B21.0 GBFits~129 tok/sQ4_K_M
Gemma 4 31B19.8 GBFits~26 tok/sQ4_K_M
Qwen 3 30B-A3B18.8 GBFits~107 tok/sQ5_K_M
Qwen3-Coder 30B-A3B18.8 GBFits~107 tok/sQ5_K_M
Gemma 3 27B17.6 GBFits~30 tok/sQ5_K_M
Qwen 3.5 27B16.9 GBFits~31 tok/sQ6_K
Qwen 3.6 27B16.9 GBFits~31 tok/sQ6_K
Qwen 3.8 27B16.9 GBFits~31 tok/sQ6_K
Mistral Small 3.1 24B15.6 GBFits~34 tok/sQ6_K
Gemma 4 26B-A4B15.6 GBFits~126 tok/sQ6_K
gpt-oss-20b12.9 GBFits~149 tok/sMXFP4 (official)
Qwen 2.5 14B10.7 GBFits~53 tok/sQ8_0
DeepSeek R1 Distill Qwen 14B10.7 GBFits~53 tok/sQ8_0
Phi-4 14B10.6 GBFits~53 tok/sQ8_0
Qwen 3 14B10.5 GBFits~54 tok/sQ8_0
Ministral 3 14B9.7 GBFits~58 tok/sQ8_0
Mistral Nemo 12B9.0 GBFits~64 tok/sQ8_0
Gemma 3 12B8.8 GBFits~64 tok/sQ8_0
Ministral 3 8B6.7 GBFits~90 tok/sFP16 / BF16
Qwen 3 8B6.6 GBFits~92 tok/sFP16 / BF16
Qwen 3.5 9B6.4 GBFits~91 tok/sFP16 / BF16
Llama 3.1 8B6.3 GBFits~95 tok/sFP16 / BF16
DeepSeek R1 Distill Llama 8B6.3 GBFits~95 tok/sFP16 / BF16
Mistral 7B v0.35.7 GBFits~104 tok/sFP16 / BF16
Gemma 4 E4B5.7 GBFits~102 tok/sFP16 / BF16
Qwen 2.5 7B5.6 GBFits~106 tok/sFP16 / BF16
DeepSeek R1 Distill Qwen 7B5.6 GBFits~106 tok/sFP16 / BF16
Qwen 3 4B4.2 GBFits~163 tok/sFP16 / BF16
Phi-4 Mini4.1 GBFits~174 tok/sFP16 / BF16
Gemma 4 E2B4.0 GBFits~160 tok/sFP16 / BF16
Gemma 3 4B3.7 GBFits~176 tok/sFP16 / BF16
Qwen 3.5 4B3.7 GBFits~182 tok/sFP16 / BF16
Ministral 3 3B3.5 GBFits~199 tok/sFP16 / BF16
Llama 3.2 3B3.5 GBFits~206 tok/sFP16 / BF16
Llama 3.2 1B1.7 GBFits~544 tok/sFP16 / BF16
Gemma 3 1B1.6 GBFits~684 tok/sFP16 / BF16
Qwen 3.5 122B-A10B70.2 GB32% on GPU~9.0 tok/s–
Mistral Small 4 119B68.2 GB33% on GPU~14 tok/s–
gpt-oss-120b67.2 GB34% on GPU~18 tok/s–
Llama 4 Scout64.0 GB35% on GPU~5.4 tok/s–
GLM-4.5-Air62.1 GB36% on GPU~7.6 tok/s–
Qwen3-Next 80B-A3B46.3 GB49% on GPU~32 tok/s–
Qwen 2.5 72B44.5 GB51% on GPU~1.7 tok/s–
Llama 3.3 70B43.2 GB52% on GPU~1.8 tok/s–
DeepSeek R1 Distill Llama 70B43.2 GB52% on GPU~1.8 tok/s–
Mixtral 8x7B28.0 GB81% on GPU~18 tok/sQ2_K
Kimi K2.6566 GBToo small––
DeepSeek R1 671B380 GBToo small––
Llama 3.1 405B234 GBToo small––
Qwen 3.5 397B-A17B226 GBToo small––
DeepSeek V4 Flash161 GBToo small––
Qwen 3 235B-A22B135 GBToo small––
MiniMax-M2133 GBToo small––

Radeon RX 7900 XTX 24GB for local AI

AMD's 24 GB flagship. It holds the same models as an RTX 3090 or 4090, and token generation is competitive, but software support for training and image tools still trails CUDA.

Setup tips: on Linux, install ROCm and use Ollama or llama.cpp built with HIP; on Windows, LM Studio's ROCm or Vulkan runtime is the easiest route. It runs the same 27–35B models as a 3090 or 4090 at Q4_K_M.

Among the 8 devices we track with about 24 GB of usable memory, the Radeon RX 7900 XTX 24GB ranks 3 of 8 for text-generation speed, because generation speed follows memory bandwidth. Others in the same class: RTX 3090 Ti 24GB, RTX 4090 24GB, RTX 3090 24GB, Intel Arc Pro B60 24GB. They all hold the same models; the faster ones simply answer sooner.

Our picks for the Radeon RX 7900 XTX 24GB

The model we would run first for each job, chosen from our recommendations by what fits fully with an 8K context. "Best quant" is the highest quality that fits; "max context" is at Q4_K_M or the native format.

TaskModelBest quantMax contextSpeed
Best all-rounderQwen 3.6 27BQ6_K98K~31 tok/s
CodingQwen 3.6 27BQ6_K98K~31 tok/s
Fastest repliesQwen 3.6 35B-A3BQ4_K_M88K~129 tok/s
Long documents (64K+)Qwen 3.8 27BQ6_K98K~31 tok/s
Images in, text outGemma 4 31BQ4_K_M43K~26 tok/s
Image generationQwen-ImageFP8––
SpecValue
Memory24 GB
Memory bandwidth960 GB/s
ArchitectureRDNA 3
Released2022-12
Llama 3.1 8B Q4_K_M speed~95 tok/s
Largest model that fitsQwen 3.5 35B-A3B

Image and video models on the Radeon RX 7900 XTX 24GB

Peak VRAM with model swapping (ComfyUI default on smaller cards).

ModelFP8FP8 fitGGUF Q4Q4 fit
Stable Diffusion 1.51.8 GBFits1.7 GBFits
SDXL 1.04.1 GBFits4.1 GBFits
Stable Diffusion 3.5 Medium6.4 GBFits6.4 GBFits
Stable Diffusion 3.5 Large8.6 GBFits6.4 GBFits
FLUX.1 [dev]12.1 GBFits7.3 GBFits
FLUX.1 [schnell]12.1 GBFits7.3 GBFits
Qwen-Image20.4 GBFits12.3 GBFits
Wan 2.2 A14B (T2V / I2V)18.8 GBFits13.2 GBFits
Wan 2.2 TI2V 5B8.0 GBFits6.5 GBFits
HunyuanVideo18.3 GBFits13.1 GBFits
FLUX.1 Kontext [dev]12.3 GBFits7.5 GBFits
FLUX.2 [dev]31.0 GB73% on GPU23.6 GBTight fit
Z-Image Turbo6.8 GBFits4.9 GBFits
Qwen-Image-Edit 250920.5 GBFits12.4 GBFits
LTX-219.2 GBFits12.6 GBFits

Upgrade path

The next step up is the Radeon AI PRO R9700 32GB with 32 GB.

Comparisons

Radeon RX 7900 XTX 24GB for LLMs: common questions

Can the Radeon RX 7900 XTX 24GB run Llama 3.3 70B?

Only partly. It needs about 43.2 GB at Q4_K_M, so about 52% fits in VRAM and the rest runs from system RAM, which is slow.

Can the Radeon RX 7900 XTX 24GB run Qwen 3 32B?

Yes. Qwen 3 32B needs about 21.4 GB at Q4_K_M with 8K context, and the best quality that fits is Q4_K_M.

Can the Radeon RX 7900 XTX 24GB run Qwen 3 14B?

Yes. Qwen 3 14B needs about 10.5 GB at Q4_K_M with 8K context, and the best quality that fits is Q8_0.

How fast is the Radeon RX 7900 XTX 24GB for LLMs?

About 95 tokens per second for an 8B model at Q4_K_M, based on its 960 GB/s memory bandwidth. Larger models scale down roughly in proportion to their size.

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