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URL: https://willitrunai.com/gpus/rx-7900-xtx-24gb

⇱ AI Models for RX 7900 XTX 24GB — What Runs on 24GB VRAM


AMD

RX 7900 XTX 24GB

RX 7000ConsumerRDNA 3PCIe 4ROCm

Operating mode

Choose the operating mode for this hardware

Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

See Full AI Tier List for RX 7900 XTX 24GB →

About this GPU for AI

The RX 7900 XTX 24GB is AMD's consumer AI flagship for RDNA 3, offering full official ROCm support alongside 24 GB of GDDR6 VRAM and nearly 1 TB/s of memory bandwidth. It competes directly with the RTX 4090 in VRAM capacity and is the go-to recommendation for AMD enthusiasts wanting a capable local inference card. The full ROCm support means PyTorch, llama.cpp ROCm, and other frameworks work out of the box on Linux.

Beyond LLMs

AI Capability Matrix

What AI tasks this GPU can handle — from text generation to image and video creation.

CapabilityStatusRepresentative ModelDetail
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Runs nativelyQwen 3 30B Q4
LLM Large (70B)
rocm-supportedhigh-vramhigh-performanceflagship

Specifications

Compute
FP1661 TFLOPS
INT8488 TOPS
ArchitectureRDNA 3
Memory
VRAM24 GB
Bandwidth960 GB/s
TypeGDDR6
General
FamilyRX 7000
SegmentConsumer
InterconnectPCIe 4
Compute PlatformROCM
MSRP$999
TDP355W

Key Features

RDNA 3 architecture (Navi 31 die, fully unlocked)24 GB GDDR6 on a 384-bit bus960 GB/s memory bandwidth96 Compute UnitsAMD Infinity Cache (96 MB L3)Official ROCm support — AMD's top consumer AI pick

For AI Workloads

Strengths
  • 24 GB VRAM matches the RTX 4090 — fits 13B FP16, 34B Q4, and larger models
  • Full official ROCm support for PyTorch, llama.cpp, and Stable Diffusion
  • 960 GB/s bandwidth rivals the RTX 4090 for decode throughput
  • Best consumer AMD option for local LLM inference on Linux
Considerations
  • ROCm is Linux-only — Windows users are limited to Vulkan inference
  • RDNA 3 ROCm ecosystem still trails CUDA in framework coverage
  • 355W TDP demands a high-quality power supply and good airflow
  • Some PyTorch operations fall back to slower CPU paths without CUDA equivalents

Architecture

RDNA 3

RDNA 3 is AMD's chiplet-based GPU architecture, combining a 5nm Graphics Compute Die (GCD) with 6nm Memory Cache Dies (MCDs). It introduces AI accelerators and a new unified compute unit design.

AI Relevance

ROCm support for RDNA 3 is maturing but lags behind NVIDIA's CUDA ecosystem. AI accelerator units provide some inference acceleration, but lack the dedicated Tensor Core equivalent found in NVIDIA GPUs.

Process: TSMC 5nm + 6nmPlatform: ROCMPrecisions: FP32, FP16, BF16, INT8

Cost vs cloud API

13.2× cheaper than Claude Sonnet / GPT-4o per token

Assumes 4 hours/day of active inference at 105 tok/s, RX 7900 XTX 24GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).

45.1M

Tokens/month at this pace

$34.1

Monthly local cost

$451

Same tokens on cloud API

$0.756

Local $/1M tokens

Break-even: pays for itself in 2.2 months vs cloud API at this workload. Price reference: $999 MSRP.

Recommendations by Workload

Chat

S

Qwen 3 14B

Qwen 3 14B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 68.3 tok/s · 60K ctx · llama.cppEST.
16.0 GB / 24.0 GB VRAM

Coding

S

Codestral 2 25.08

Codestral 2 25.08 is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.

Decode 52.0 tok/s · 48K ctx · llama.cppEST.
19.2 GB / 24.0 GB VRAM

Agentic Coding

S

Full Model Compatibility

👁 Alibaba
Qwen3-Coder 30B A3B Instruct
S97
30.5B23.4 GB105 tok/s23K ctx
moe
👁 Alibaba
Qwen3-VL 30B A3B Instruct
S96
30B23.1 GB108 tok/s26K ctx
moe
👁 OpenAI
GPT-OSS 20B
S95

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

Image & Video Generation

Diffusion Model Compatibility

41 of 52 models can generate images or video on your RX 7900 XTX 24GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512700msS
Stable Diffusion 1.5Image512×768~1.4sS
Realistic Vision v5.1Image512×768~1.4sS
DreamShaper 8Image512×768~1.4sS
LCM DreamShaper v7

Upgrade paths

Upgrade from RX 7900 XTX 24GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

MacBook Pro M4 Max 36GBNext step up
36 GB Unified (+12)
A
Unlocks 1 additional models that do not fit on the current setup.Unlocks Gemma 4 31B

Unlocks 1 additional models that do not fit on the current setup.

~$2,499 MSRP

Radeon Pro W7800 32GBAMD upgrade
32 GB VRAM (+8)
A
Unlocks 6 additional models that do not fit on the current setup.Unlocks Gemma 4 31B, Kimi Linear 48B A3B, Falcon 40B Instruct+3 more

Unlocks 6 additional models that do not fit on the current setup.

~$2,499 MSRP

Mac mini M4 64GBBest value
64 GB Unified (+40)
B
Unlocks 17 additional models that do not fit on the current setup.Unlocks Qwen 2.5 VL 72B, Gemma 4 31B, Llama 3.3 70B+14 more

Unlocks 17 additional models that do not fit on the current setup.

~$1,099 MSRP

AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+264)8000 GB/s (+7040)
B
Unlocks 45 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Devstral 2 123B Instruct, Qwen 3.5 122B A10B+42 more · +115% faster avg

Unlocks 45 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 115%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

RX 7900 XTX 24GB vs RTX 3090 24GBRX 7900 XTX 24GB vs RTX 3090 Ti 24GBRX 7900 XTX 24GB vs RTX 4090 24GB

Related guides

Best AI Models for 24GB VRAM — RTX 4090 & RTX 5090 (LLMs, Image & Video)What Can You Run on 16GB, 24GB, 32GB VRAM? — Local LLM Guide (April 2026)Best GPU for Running LLMs Locally (2026) — RTX 4060 to H100 Buyer's Guide
Compare this GPUCompare with another GPU →
24GB
VRAM
960GB/s
Bandwidth
61TFLOPS
FP16 Compute
488TOPS
INT8 Inference
355W TDP$999 MSRP
RX 7900 XTX 24GBCategory AvgMacBook Pro M4 Max 36GB
Won’t fit
Llama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16~~5.7s per image
Image Gen (Flux)Runs with offloadFlux.1 Dev FP16~~25.5s per image
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16~~31.2s per image
Video Short (25f)Runs nativelyLTX Video 2B~~4.9s/frame
Video Long (100f)Won't fitWan Video 14B~~14.5s/frame

Qwen 3.6 27B

Qwen 3.6 27B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.

Decode 29.8 tok/s · 69K ctx · llama.cppEST.
21.7 GB / 24.0 GB VRAM

Reasoning

S

Qwen 3 14B

Qwen 3 14B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 87.4 tok/s · 80K ctx · llama.cppEST.
14.3 GB / 24.0 GB VRAM

RAG

A

Granite 4.1 8B

Granite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.

Decode 112.0 tok/s · 93K ctx · llama.cppEST.
14.7 GB / 24.0 GB VRAM
21B18.6 GB133 tok/s52K ctx
moe
👁 Alibaba
Qwen 3 14B
S95
14B14.3 GB87 tok/s80K ctx
dense
👁 Microsoft
Phi-4-reasoning-plus 14B
S95
14.7B15.3 GB83 tok/s33K ctx
dense
👁 Alibaba
Qwen 3 30B A3B
S94
30.5B23.4 GB105 tok/s23K ctx
moe
👁 Alibaba
Qwen 3.5 27B
S94
27B22.9 GB45 tok/s21K ctx
dense
👁 Mistral
Magistral Small 2507
S93
24B20.4 GB51 tok/s40K ctx
dense
👁 Mistral
Devstral Small 2 24B Instruct
S93
24B20.4 GB51 tok/s40K ctx
dense
👁 Alibaba
Qwen 3.5 9B
S93
9B11.0 GB126 tok/s111K ctx
dense
👁 Alibaba
Qwen 3.6 27B
S93
27B20.7 GB30 tok/s69K ctx
+1dense
👁 Mistral
Devstral Small 1.1
S91
24B20.4 GB51 tok/s40K ctx
dense
👁 Alibaba
Qwen 3 8B
S91
8B10.4 GB112 tok/s115K ctx
dense
👁 NVIDIA
Nemotron Cascade 2 30B A3B
S91
30B24.5 GB77 tok/s13K ctx
moe
👁 NVIDIA
Nemotron 3 Nano 30B
S90
30B24.0 GB30 tok/s16K ctx
dense
👁 Mistral
Ministral 3 14B
S90
14B14.3 GB87 tok/s80K ctx
multimodal
👁 Google
Gemma 4 26B A4B
S89
25.2B22.3 GB112 tok/s23K ctx
moe
👁 Alibaba
Qwen 3.5 4B
S87
4B7.9 GB56 tok/s131K ctx
dense
👁 NVIDIA
Nemotron Nano 8B
S86
8B10.1 GB112 tok/s130K ctx
dense
👁 Alibaba
Qwen 3.5 35B A3B
A84
35B26.1 GB60 tok/s4K ctx
moe
👁 Microsoft
Phi-4 Mini Reasoning 4B
A84
3.8B7.1 GB53 tok/s131K ctx
dense
👁 Alibaba
Qwen 3 32B
A80
32B26.7 GB23 tok/s5K ctx
dense
👁 Alibaba
Qwen 3.6 35B A3B
A78
35B28.8 GB45 tok/s4K ctx
+1moe
👁 Jina AI
Jina Embeddings v3
A77
0.57B6.4 GB8 tok/s8K ctx
dense
👁 BAAI
BGE M3
A75
0.57B5.6 GB8 tok/s8K ctx
dense
👁 LG AI
EXAONE 4.0 32B
A74
32B26.7 GB23 tok/s5K ctx
dense
👁 Alibaba
Qwen 3.5 397B A17B
F0
397B248.3 GB2 tok/s4K ctx
moe
👁 Mistral
Devstral 2 123B Instruct
F0
123B83.7 GB2 tok/s4K ctx
dense
👁 Moonshot AI
Kimi K2.5
F0
1000B620.7 GB2 tok/s4K ctx
moe
👁 Moonshot AI
Kimi K2.6
F0
1000B620.7 GB2 tok/s4K ctx
+1moe
👁 DeepSeek
DeepSeek V4 Pro
F0
1600B867.2 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 122B A10B
F0
122B80.2 GB4 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V4 Flash
F0
284B162.6 GB2 tok/s4K ctx
moe
👁 Mistral
Mistral Small 4 119B
F0
119B81.3 GB5 tok/s4K ctx
moe
👁 Cohere
Command A 111B
F0
111B74.9 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen 2.5 VL 72B
F0
72B52.1 GB3 tok/s4K ctx
dense
👁 OpenAI
GPT-OSS 120B
F0
117B79.6 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen3-Coder-Next
F0
80B53.6 GB7 tok/s4K ctx
moe
👁 Z.ai
GLM-5.1
F0
754B482.3 GB2 tok/s4K ctx
moe
👁 Mistral AI
Pixtral Large 124B
F0
124B84.3 GB2 tok/s4K ctx
dense
👁 Z.ai
GLM-5
F0
744B476.2 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.2
F0
671B413.1 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3 235B A22B
F0
235B149.5 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B299.0 GB2 tok/s4K ctx
moe
👁 Google
Gemma 4 31B
F0
30.7B36.7 GB8 tok/s4K ctx
dense
MiniMax M2.7
F0
230B147.4 GB2 tok/s4K ctx
moe
👁 Mistral
Leanstral 119B A6B
F0
119B84.7 GB4 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek Coder V2 236B
F0
236B205.9 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek R1 671B
F0
671B472.2 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.1 671B
F0
671B472.2 GB2 tok/s4K ctx
moe
Image
512×768
400ms
S
PixArt-SigmaImage1024×1024~5.7sS
FramePack I2VVideo256×256~10.4s/frameS
SDXL TurboImage512×512700msS
SDXL LightningImage1024×1024~2.1sS
Stable Diffusion XL 1.0Image1024×1024~5.7sS
Playground v2.5Image1024×1024~8.5sS
RealVisXL v5.0Image1024×1024~6.4sS
DreamShaper XLImage1024×1024~6.4sS
Juggernaut XL v9Image1024×1024~6.4sS
Animagine XL 3.1Image1024×1024~6.4sS
Pony Diffusion V6 XLImage1024×1024~6.4sS
Animagine XL 4.0Image1024×1024~6.4sS
Illustrious XLImage1024×1024~6.4sS
Wan Video 2.1 1.3BVideo256×256~4.1s/frameS
Stable Diffusion 3.5 MediumImage1024×1024~9.9sS
Flux.2 Klein 4BImage1024×1024~1.7sS
LTX Video 2BVideo768×512~4.9s/frameS
KolorsImage1024×1024~11.4sS
Stable CascadeImage1024×1024~14.2sS
AuraFlow v0.3Image1536×1536~25.5sS
Stable Diffusion 3.5 LargeImage1024×1024~31.2sS
Stable Diffusion 3.5 Large TurboImage1024×1024~5.7sS
CogVideoX 2BVideo720×480~4.9s/frameA
HunyuanVideoVideo256×256~10.4s/frameA
ChromaImage256×256~10.4sA
Z-Image TurboImage1536×1536~5.9sB
Flux.1 DevImage256×256~25.5sB
Flux.1 SchnellImage256×256~5sB
LTX Video 13BVideo256×256~10.4s/frameB
Flux.1 Kontext DevImage256×256~28.4sB
AnimateDiff v1.5.3Video512×768~2.6s/frameB
Cosmos Diffusion 7BVideo256×256~15.7s/frameB
CogVideoX 5BVideo256×256~14.9s/frameB
Wan2.2 TI2V 5BVideo256×256~14.9s/frameB
Flux.2 Klein 9BImage256×256~5.2sD
Flux.1 Fill DevImage256×256~24.1sD
Mochi 1 PreviewVideo256×256~9.4s/frameF
HunyuanVideo 1.5Video256×256~8.7s/frameF
Helios 14BVideo256×256~10.7s/frameF
SkyReels V2 14BVideo256×256~10.7s/frameF
Wan Video 2.1 14BVideo256×256~10.7s/frameF
Wan Video 2.2 14BVideo256×256~10.7s/frameF
Qwen ImageImage256×256~9.6sF
Qwen Image EditImage256×256~9.6sF
Flux.2 DevImage256×256~4m 29sF
MAGI-1Video256×256~13.3s/frameF
HunyuanImage 3.0Image256×256~16.8sF

Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.

Buying advice

Should you buy RX 7900 XTX 24GB for local AI?

Excellent choice for local AI

Runs 26 of 50 top models well — a strong all-rounder for local inference.

24.0 GB

VRAM

$999

MSRP

$42/GB

Cost per GB VRAM

Best models for this GPU

What will limit you first

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best upgrade itinerary

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Unlocks 1 additional models that do not fit on the current setup.

Want more headroom? MacBook Pro M4 Max 36GB (36.0 GB unified memory) is the next step up.