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URL: https://willitrunai.com/gpus/rx-9060-8gb

⇱ AI Models for RX 9060 8GB — What Runs on 8GB VRAM


AMD

RX 9060 8GB

RX 9000ConsumerRDNA 4PCIe 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 9060 8GB →

About this GPU for AI

The RX 9060 8GB is an entry-level RDNA 4 card, AMD's newest consumer architecture as of 2025. RDNA 4 is expected to have ROCm support but the ecosystem is still early — driver stability and framework compatibility should be verified before assuming production-ready AI use. Its 8 GB of GDDR6 VRAM limits it to smaller models, but the RDNA 4 architecture brings improved compute efficiency over RDNA 3.

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)Won’t fitQwen 3 30B Q4
LLM Large (70B)
rdna4-earlybudget-friendlysoftware-limited

Specifications

Compute
FP1645 TFLOPS
INT890 TOPS
ArchitectureRDNA 4
Memory
VRAM8 GB
Bandwidth288 GB/s
General
FamilyRX 9000
SegmentConsumer
InterconnectPCIe 4
Compute PlatformROCM
MSRP$249

Key Features

RDNA 4 architecture (Navi 44 die)8 GB GDDR6 on a 128-bit bus288 GB/s memory bandwidthPCIe Gen 4 x8Improved AI accelerators vs RDNA 3ROCm support expected — ecosystem still maturing

For AI Workloads

Strengths
  • RDNA 4 brings improved AI accelerator efficiency over prior generations
  • Budget-friendly entry point ($249) for AMD's latest architecture
  • ROCm support expected on RDNA 4 — better long-term software prospects than RDNA 2
  • Low power draw for a modern GPU
Considerations
  • RDNA 4 ROCm ecosystem is very new — verify compatibility before purchasing for AI
  • 8 GB VRAM caps usable models at 7B Q4 — a hard limitation
  • Framework coverage for RDNA 4 ROCm lags behind NVIDIA CUDA
  • Early adopters may face driver and kernel compatibility issues

Architecture

RDNA 4

RDNA 4 is AMD's latest GPU architecture built on TSMC 4nm. It focuses on efficiency and ray tracing improvements with enhanced AI processing capabilities.

AI Relevance

Improved ROCm support and new AI accelerators with FP8 support bring AMD closer to competitive AI inference performance. The focus on efficiency makes RDNA 4 GPUs attractive for power-constrained deployments.

Process: TSMC 4nmPlatform: ROCMPrecisions: FP32, FP16, BF16, FP8, INT8

Recommendations by Workload

Chat

S

Qwen 3.5 4B

Qwen 3.5 4B 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 56.0 tok/s · 22K ctx · llama.cppEST.
6.1 GB / 8.0 GB VRAM

Coding

A

Codestral Mamba 7B

Codestral Mamba 7B is a specialized fit for Coding. 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 48.9 tok/s · 67K ctx · llama.cppEST.
6.5 GB / 8.0 GB VRAM

Agentic Coding

A

Full Model Compatibility

👁 Alibaba
Qwen 3.5 4B
S95
4B6.3 GB56 tok/s28K ctx
dense
👁 Microsoft
Phi-4 Mini Reasoning 4B
S92
3.8B5.5 GB53 tok/s43K ctx
dense
👁 Jina AI
Jina Embeddings v3
A84

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

21 of 52 models can generate images or video on your RX 9060 8GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~1.1sS
Stable Diffusion 1.5Image512×768~2.2sS
Realistic Vision v5.1Image512×768~2.2sS
DreamShaper 8Image512×768~2.2sS
LCM DreamShaper v7

Upgrade paths

Upgrade from RX 9060 8GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

👁 NVIDIA
RTX 3080 10GBNext step up
10 GB VRAM (+2)760 GB/s (+472)
A
Unlocks 33 additional models that do not fit on the current setup.Unlocks Qwen 3 14B, Ministral 3 14B, Phi-4 14B+30 more · +75% faster avg

Unlocks 33 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 75%.

~$699 MSRP

RX 7700 XT 12GBAMD upgrade
12 GB VRAM (+4)432 GB/s (+144)
A
Unlocks 37 additional models that do not fit on the current setup.Unlocks Qwen 3 14B, Phi-4-reasoning-plus 14B, Ministral 3 14B+34 more · +27% faster avg

Unlocks 37 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 27%.

~$449 MSRP

RX 7600 XT 16GBBest value
16 GB VRAM (+8)
A
Unlocks 74 additional models that do not fit on the current setup.Unlocks Magistral Small 2507, Devstral Small 2 24B Instruct, Qwen 3 14B+71 more

Unlocks 74 additional models that do not fit on the current setup.

~$329 MSRP

AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+280)8000 GB/s (+7712)
B
Unlocks 155 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 397B A17B, Devstral 2 123B Instruct+152 more · +335% faster avg

Unlocks 155 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 335%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

RX 9060 8GB vs RTX 3050 8GBRX 9060 8GB vs RTX 3060 Ti 8GBRX 9060 8GB vs RTX 3070 8GB
Compare this GPUCompare with another GPU →
8GB
VRAM
288GB/s
Bandwidth
45TFLOPS
FP16 Compute
90TOPS
INT8 Inference
$249 MSRP
RX 9060 8GBCategory AvgRTX 3080 10GB
Won’t fit
Llama 3.1 70B Q4
Image Gen (SDXL)Runs with sequential offloadSDXL 1.0 FP16~~23.3s per image
Image Gen (Flux)Won't fitFlux.1 Dev FP16~~39.6s per image
Image Gen (SD 3.5)Won't fitSD 3.5 Large FP16~~48.4s per image
Video Short (25f)Won't fitLTX Video 2B~~7.6s/frame
Video Long (100f)Won't fitWan Video 14B~~22.5s/frame

Gemma 4 E2B

Gemma 4 E2B is a specialized fit for Agentic 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, ollama, lm-studio.

Decode 48.1 tok/s · 96K ctx · llama.cppEST.
5.9 GB / 8.0 GB VRAM

Reasoning

A

Codestral Mamba 7B

Codestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. 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 48.9 tok/s · 67K ctx · llama.cppEST.
6.5 GB / 8.0 GB VRAM

RAG

A

Granite 4.1 3B

Granite 4.1 3B 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 42.0 tok/s · 59K ctx · llama.cppEST.
6.0 GB / 8.0 GB VRAM
0.57B4.8 GB8 tok/s8K ctx
dense
👁 BAAI
BGE M3
A81
0.57B4.0 GB8 tok/s8K ctx
dense
👁 Alibaba
Qwen 3.5 9B
A80
9B9.4 GB19 tok/s6K ctx
dense
👁 Alibaba
Qwen 3 8B
A80
8B8.8 GB25 tok/s10K ctx
dense
👁 NVIDIA
Nemotron Nano 8B
A75
8B8.5 GB26 tok/s12K ctx
dense
👁 Alibaba
Qwen3-Coder 30B A3B Instruct
F0
30.5B21.8 GB4 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 397B A17B
F0
397B246.7 GB2 tok/s4K ctx
moe
👁 Mistral
Devstral 2 123B Instruct
F0
123B82.1 GB2 tok/s4K ctx
dense
👁 Moonshot AI
Kimi K2.5
F0
1000B619.1 GB2 tok/s4K ctx
moe
👁 Moonshot AI
Kimi K2.6
F0
1000B619.1 GB2 tok/s4K ctx
+1moe
👁 DeepSeek
DeepSeek V4 Pro
F0
1600B865.6 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 27B
F0
27B21.3 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen 3.6 27B
F0
27B19.1 GB2 tok/s4K ctx
+1dense
👁 Alibaba
Qwen 3.5 122B A10B
F0
122B78.6 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen3-VL 30B A3B Instruct
F0
30B21.5 GB4 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.6 35B A3B
F0
35B27.2 GB4 tok/s4K ctx
+1moe
👁 DeepSeek
DeepSeek V4 Flash
F0
284B161.0 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 35B A3B
F0
35B24.5 GB4 tok/s4K ctx
moe
👁 Mistral
Magistral Small 2507
F0
24B18.8 GB2 tok/s4K ctx
dense
👁 Mistral
Devstral Small 2 24B Instruct
F0
24B18.8 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen 3 32B
F0
32B25.1 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen 3 14B
F0
14B12.7 GB7 tok/s4K ctx
dense
👁 Alibaba
Qwen 3 30B A3B
F0
30.5B21.8 GB4 tok/s4K ctx
moe
👁 Mistral
Mistral Small 4 119B
F0
119B79.7 GB2 tok/s4K ctx
moe
👁 Cohere
Command A 111B
F0
111B73.3 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen 2.5 VL 72B
F0
72B50.5 GB2 tok/s4K ctx
dense
👁 OpenAI
GPT-OSS 120B
F0
117B78.0 GB2 tok/s4K ctx
dense
👁 NVIDIA
Nemotron 3 Nano 30B
F0
30B22.4 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen3-Coder-Next
F0
80B52.0 GB2 tok/s4K ctx
moe
👁 Microsoft
Phi-4-reasoning-plus 14B
F0
14.7B13.7 GB5 tok/s4K ctx
dense
👁 Mistral
Devstral Small 1.1
F0
24B18.8 GB2 tok/s4K ctx
dense
👁 Z.ai
GLM-5.1
F0
754B480.7 GB2 tok/s4K ctx
moe
👁 Mistral AI
Pixtral Large 124B
F0
124B82.7 GB2 tok/s4K ctx
dense
👁 Z.ai
GLM-5
F0
744B474.6 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.2
F0
671B411.5 GB2 tok/s4K ctx
moe
👁 OpenAI
GPT-OSS 20B
F0
21B17.0 GB5 tok/s4K ctx
moe
👁 Alibaba
Qwen 3 235B A22B
F0
235B147.9 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B297.4 GB2 tok/s4K ctx
moe
👁 NVIDIA
Nemotron Cascade 2 30B A3B
F0
30B22.9 GB4 tok/s4K ctx
moe
👁 Google
Gemma 4 31B
F0
30.7B35.1 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B145.8 GB2 tok/s4K ctx
moe
👁 Mistral
Leanstral 119B A6B
F0
119B83.1 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek Coder V2 236B
F0
236B204.3 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek R1 671B
F0
671B470.6 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.1 671B
F0
671B470.6 GB2 tok/s4K ctx
moe
👁 Mistral
Ministral 3 14B
F0
14B12.7 GB7 tok/s4K ctx
multimodal
👁 LG AI
EXAONE 4.0 32B
F0
32B25.1 GB2 tok/s4K ctx
dense
👁 Google
Gemma 4 26B A4B
F0
25.2B20.7 GB4 tok/s4K ctx
moe
Image
512×768
700ms
S
PixArt-SigmaImage256×256~8.8sS
FramePack I2VVideo256×256~16.1s/frameA
SDXL TurboImage256×256~2.9sA
SDXL LightningImage256×256~8.8sB
Stable Diffusion XL 1.0Image256×256~23.3sB
Playground v2.5Image256×256~13.2sB
RealVisXL v5.0Image256×256~26.3sB
DreamShaper XLImage256×256~26.3sB
Juggernaut XL v9Image256×256~26.3sB
Animagine XL 3.1Image256×256~26.3sB
Pony Diffusion V6 XLImage256×256~26.3sB
Animagine XL 4.0Image256×256~26.3sB
Illustrious XLImage256×256~26.3sB
Wan Video 2.1 1.3BVideo256×256~6.4s/frameD
Stable Diffusion 3.5 MediumImage256×256~15.4sD
Flux.2 Klein 4BImage256×256~2.6sD
LTX Video 2BVideo256×256~7.6s/frameF
KolorsImage256×256~17.6sF
Stable CascadeImage256×256~22sF
AuraFlow v0.3Image256×256~39.6sF
Stable Diffusion 3.5 LargeImage256×256~48.4sF
Stable Diffusion 3.5 Large TurboImage256×256~8.8sF
CogVideoX 2BVideo256×256~7.6s/frameF
HunyuanVideoVideo256×256~16.1s/frameF
ChromaImage256×256~8.8sF
Z-Image TurboImage256×256~9.1sF
Flux.1 DevImage256×256~39.6sF
Flux.1 SchnellImage256×256~7.7sF
LTX Video 13BVideo256×256~16.1s/frameF
Flux.1 Kontext DevImage256×256~44sF
AnimateDiff v1.5.3Video512×768~4s/frameF
Cosmos Diffusion 7BVideo256×256~12.6s/frameF
CogVideoX 5BVideo256×256~11s/frameF
Wan2.2 TI2V 5BVideo256×256~11s/frameF
Flux.2 Klein 9BImage256×256~4.4sF
Flux.1 Fill DevImage256×256~37.4sF
Mochi 1 PreviewVideo256×256~14.5s/frameF
HunyuanVideo 1.5Video256×256~13.5s/frameF
Helios 14BVideo256×256~16.6s/frameF
SkyReels V2 14BVideo256×256~16.6s/frameF
Wan Video 2.1 14BVideo256×256~16.6s/frameF
Wan Video 2.2 14BVideo256×256~16.6s/frameF
Qwen ImageImage256×256~14.8sF
Qwen Image EditImage256×256~14.8sF
Flux.2 DevImage256×256~6m 56sF
MAGI-1Video256×256~20.6s/frameF
HunyuanImage 3.0Image256×256~26.1sF

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.

There are 4 upgrade path(s) from RX 9060 8GB: RTX 3080 10GB, RX 7700 XT 12GB. Upgrading would unlock larger models and faster inference speeds.

Buying advice

Should you buy RX 9060 8GB for local AI?

Usable for local AI with limits

Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.

8.0 GB

VRAM

$249

MSRP

$31/GB

Cost per GB VRAM

Best models for this GPU

What will limit you first

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best upgrade itinerary

Unlocks 33 additional models that do not fit on the current setup.

Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.