AMD

AMD Instinct MI60 32GB

InstinctDatacenterVegaPCIe 4ROCm
32GB
VRAM
1kGB/s
Bandwidth
29TFLOPS
FP16 Compute
58TOPS
INT8 Inference
$8,999 MSRP
VRAM32 GBBandwidth1k GB/sCompute29 TFInference58 TOPSValue0.32 TF/$k
AMD Instinct MI60 32GBCategory AvgMacBook Pro M1 Max 64GB

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.

About this GPU for AI

The AMD Instinct MI60 32GB is an older Vega-based datacenter GPU from 2018, one of AMD's first serious HPC accelerators. While it has full ROCm support (being a datacenter Instinct card), the Vega architecture is old and the 29 TFLOPS FP16 compute is very modest by modern standards. The 32 GB of HBM2 VRAM is its main AI asset, but newer Instinct cards offer dramatically better compute at lower cost on the used market.

Beyond LLMs

AI Capability Matrix

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

CapabilityStatusRepresentative Model
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Runs nativelyQwen 3 30B Q4
LLM Large (70B)Won’t fitLlama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16
Image Gen (Flux)Runs nativelyFlux.1 Dev FP16
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16
Video Short (25f)Runs nativelyLTX Video 2B
Video Long (100f)Won't fitWan Video 14B
rocm-supporteddatacenter-gradelegacyhigh-bandwidth

Spezifikationen

Rechenleistung
FP1629 TFLOPS
INT858 TOPS
ArchitekturVega
Speicher
VRAM32 GB
Bandbreite1024 GB/s
Allgemein
FamilieInstinct
SegmentDatacenter
InterconnectPCIe 4
Compute-PlattformROCM
MSRP$8,999

Hauptmerkmale

Vega (GCN 5) architecture — AMD's HPC-focused Vega 20 die32 GB HBM2 on a 4096-bit bus1 TB/s memory bandwidthFull ROCm support — Instinct datacenter cardPCIe Gen 3/4 x16Legacy ROCm support may require older toolchain versions

Für KI-Workloads

Stärken
  • Full ROCm support as an Instinct datacenter card
  • 32 GB HBM2 with 1 TB/s bandwidth — memory bandwidth is a strength
  • HBM2 delivers very high bandwidth for memory-bandwidth-bound inference
  • Full ROCm software stack compatible
Hinweise
  • 29 TFLOPS FP16 is very low compute — slow token generation
  • Vega architecture is significantly older than CDNA — less efficient AI kernels
  • Newer ROCm versions may drop or reduce support for legacy Vega
  • MI100 or MI210 are far better choices for actual AI workloads

Architecture

Vega

Vega is AMD's GCN 5th generation architecture, featuring HBM2 memory and high compute density. Used in consumer Vega cards and the Instinct MI60 datacenter accelerator.

AI Relevance

The Instinct MI60 with 32 GB HBM2 and ROCm support can run LLM inference, but its age means limited compatibility with modern AI frameworks. Consumer Vega cards have insufficient VRAM for meaningful AI work.

Process: GlobalFoundries 14nmPlatform: ROCMPrecisions: FP64, FP32, FP16

Kaufberatung

Sollten Sie AMD Instinct MI60 32GB für lokale KI kaufen?

Ausgezeichnete Wahl für lokale KI

Führt 27 von 50 Top-Modellen gut aus — ein starker Allrounder für lokale Inferenz.

32.0 GB

VRAM

$8,999

UVP

$281/GB

Kosten pro GB VRAM

Beste Modelle für diese 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 11 additional models that do not fit on the current setup.

Mehr Spielraum gewünscht? MacBook Pro M1 Max 64GB (64.0 GB unified memory) ist die nächste Stufe.

Recommendations by Workload

Chat

S

Qwen 3 30B A3B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 75.9 tok/s · 102K ctx · llama.cppEST.
23.4 GB / 32.0 GB VRAM

Coding

S

Qwen 3.6 27B

This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.

Decode 20.5 tok/s · 187K ctx · llama.cppEST.
21.5 GB / 32.0 GB VRAM

Agentic Coding

S

Qwen 3.6 27B

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.

Decode 20.5 tok/s · 187K ctx · llama.cppEST.
22.5 GB / 32.0 GB VRAM

Reasoning

S

Devstral Small 2 24B Instruct

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 36.8 tok/s · 87K ctx · llama.cppEST.
21.2 GB / 32.0 GB VRAM

RAG

S

Qwen 3.5 27B

This model is a direct match for rag. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.

Decode 32.9 tok/s · 58K ctx · llama.cppEST.
26.9 GB / 32.0 GB VRAM

Full Model Compatibility

AlibabaQwen3-Coder 30B A3B Instruct
S99
30.5B24.2 GB76 tok/s102K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
S99
30B23.9 GB79 tok/s105K ctx
moe
AlibabaQwen 3 30B A3B
S97
30.5B24.2 GB76 tok/s102K ctx
moe
AlibabaQwen 3.5 27B
S96
27B23.7 GB33 tok/s58K ctx
dense
AlibabaQwen 3.6 35B A3B
S95
35B29.6 GB64 tok/s26K ctx
+1moe
MistralMagistral Small 2507
S95
24B21.2 GB37 tok/s87K ctx
dense
AlibabaQwen 3.5 35B A3B
S95
35B26.9 GB69 tok/s72K ctx
moe
MistralDevstral Small 2 24B Instruct
S94
24B21.2 GB37 tok/s87K ctx
dense
NVIDIANemotron Cascade 2 30B A3B
S94
30B25.3 GB78 tok/s52K ctx
moe
AlibabaQwen 3.6 27B
S94
27B21.5 GB21 tok/s187K ctx
+1dense
OpenAIGPT-OSS 20B
S93
21B19.4 GB96 tok/s99K ctx
moe
NVIDIANemotron 3 Nano 30B
S93
30B24.8 GB30 tok/s63K ctx
dense
MistralDevstral Small 1.1
S93
24B21.2 GB37 tok/s87K ctx
dense
GoogleGemma 4 26B A4B
S92
25.2B23.1 GB82 tok/s55K ctx
moe
AlibabaQwen 3 14B
S92
14B15.1 GB64 tok/s127K ctx
dense
AlibabaQwen 3.5 9B
S91
9B11.8 GB98 tok/s131K ctx
dense
AlibabaQwen 3 32B
S91
32B27.5 GB28 tok/s34K ctx
dense
MicrosoftPhi-4-reasoning-plus 14B
S91
14.7B16.1 GB60 tok/s33K ctx
dense
AlibabaQwen 3 8B
S89
8B11.2 GB111 tok/s131K ctx
dense
AlibabaQwen 3.5 4B
S86
4B8.7 GB56 tok/s131K ctx
dense
MistralMinistral 3 14B
S86
14B15.1 GB63 tok/s127K ctx
multimodal
LG AIEXAONE 4.0 32B
S85
32B27.5 GB28 tok/s34K ctx
dense
NVIDIANemotron Nano 8B
A84
8B10.9 GB111 tok/s131K ctx
dense
MicrosoftPhi-4 Mini Reasoning 4B
A83
3.8B7.9 GB53 tok/s131K ctx
dense
Jina AIJina Embeddings v3
A76
0.57B7.2 GB8 tok/s8K ctx
dense
BAAIBGE M3
A74
0.57B6.4 GB8 tok/s8K ctx
dense
GoogleGemma 4 31B
A73
30.7B37.5 GB9 tok/s10K ctx
dense
AlibabaQwen 3.5 397B A17B
F0
397B249.1 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B84.5 GB2 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B621.5 GB2 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B621.5 GB2 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B868.0 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 122B A10B
F0
122B81.0 GB3 tok/s4K ctx
moe
DeepSeekDeepSeek V4 Flash
F0
284B163.4 GB2 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B82.1 GB3 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B75.7 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B52.9 GB3 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B80.4 GB2 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B54.4 GB8 tok/s4K ctx
moe
Z.aiGLM-5.1
F0
754B483.1 GB2 tok/s4K ctx
moe
Mistral AIPixtral Large 124B
F0
124B85.1 GB2 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B477.0 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B413.9 GB2 tok/s4K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B150.3 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B299.8 GB2 tok/s4K ctx
moe
MiniMax M2.7
F0
230B148.2 GB2 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B85.5 GB3 tok/s4K ctx
moe
DeepSeekDeepSeek Coder V2 236B
F0
236B206.7 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B473.0 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B473.0 GB2 tok/s4K ctx
moe

Fast erreichbar

Modelle, die Sie mit einem Upgrade ausführen könnten

Hochwertige Modelle, die etwas mehr Speicher benötigen

1000BStufe 100Benötigt ca. 617.8 GB
1000BStufe 100Benötigt ca. 617.8 GB

Image & Video Generation

Diffusion Model Compatibility

43 of 52 models can generate images or video on your AMD Instinct MI60 32GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~2.2sS
Stable Diffusion 1.5Image512×768~4.4sS
Realistic Vision v5.1Image512×768~4.4sS
DreamShaper 8Image512×768~4.4sS
LCM DreamShaper v7Image512×768~1.3sS
PixArt-SigmaImage1024×1024~17.5sS
FramePack I2VVideo256×256~32.2s/frameS
SDXL TurboImage512×512~2.2sS
SDXL LightningImage1024×1024~6.6sS
Stable Diffusion XL 1.0Image1024×1024~17.5sS
Playground v2.5Image1024×1024~26.3sS
RealVisXL v5.0Image1024×1024~19.7sS
DreamShaper XLImage1024×1024~19.7sS
Juggernaut XL v9Image1024×1024~19.7sS
Animagine XL 3.1Image1024×1024~19.7sS
Pony Diffusion V6 XLImage1024×1024~19.7sS
Animagine XL 4.0Image1024×1024~19.7sS
Illustrious XLImage1024×1024~19.7sS
Wan Video 2.1 1.3BVideo480×832~12.8s/frameS
Stable Diffusion 3.5 MediumImage1024×1024~30.7sS
Flux.2 Klein 4BImage1024×1024~5.3sS
LTX Video 2BVideo1280×720~15.2s/frameS
KolorsImage1024×1024~35.1sS
Stable CascadeImage1024×1024~43.9sS
AuraFlow v0.3Image1536×1536~1m 19sS
Stable Diffusion 3.5 LargeImage1024×1024~1m 37sS
Stable Diffusion 3.5 Large TurboImage1024×1024~17.5sS
CogVideoX 2BVideo720×480~15.2s/frameS
HunyuanVideoVideo256×256~32.2s/frameS
ChromaImage1024×1024~17.5sS
Z-Image TurboImage1536×1536~18.1sS
Flux.1 DevImage256×256~2m 18sS
Flux.1 SchnellImage256×256~26.9sS
LTX Video 13BVideo256×256~32.2s/frameS
Flux.1 Kontext DevImage256×256~2m 34sS
AnimateDiff v1.5.3Video512×768~8s/frameS
Cosmos Diffusion 7BVideo1024×576~25.1s/frameA
CogVideoX 5BVideo720×480~22s/frameA
Wan2.2 TI2V 5BVideo832×480~22s/frameA
Flux.2 Klein 9BImage1024×1024~8.8sA
Flux.1 Fill DevImage256×256~2m 11sB
Mochi 1 PreviewVideo256×256~52.2s/frameD
HunyuanVideo 1.5Video256×256~50.1s/frameD
Helios 14BVideo256×256~33.2s/frameF
SkyReels V2 14BVideo256×256~33.2s/frameF
Wan Video 2.1 14BVideo256×256~33.2s/frameF
Wan Video 2.2 14BVideo256×256~33.2s/frameF
Qwen ImageImage256×256~29.5sF
Qwen Image EditImage256×256~29.5sF
Flux.2 DevImage256×256~13m 50sF
MAGI-1Video256×256~41.2s/frameF
HunyuanImage 3.0Image256×256~52sF

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.

Upgrade paths

Upgrade from AMD Instinct MI60 32GB

See what you unlock with more powerful hardware

Upgrade-Optionen

Upgrade-Optionen

Frequently Asked Questions

What AI models can I run on AMD Instinct MI60 32GB?

AMD Instinct MI60 32GB (32 GB VRAM) can run these top models: Qwen3-Coder 30B A3B Instruct (score: 99/100), Qwen3-VL 30B A3B Instruct (score: 99/100), Qwen 3 30B A3B (score: 97/100). See the full compatibility list above.

How much VRAM does AMD Instinct MI60 32GB have for AI?

AMD Instinct MI60 32GB has 32 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is AMD Instinct MI60 32GB good for running LLMs locally?

Yes, AMD Instinct MI60 32GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for AMD Instinct MI60 32GB for coding?

For coding on AMD Instinct MI60 32GB, we recommend Qwen 3.6 27B. It achieves 20.5 tokens per second with 187K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, lm-studio.

Should I upgrade from AMD Instinct MI60 32GB?

There are 4 upgrade path(s) from AMD Instinct MI60 32GB: MacBook Pro M1 Max 64GB, Radeon PRO W7900 DS 48GB. Upgrading would unlock larger models and faster inference speeds.

Can AMD Instinct MI60 32GB run Flux for image generation?

Yes, AMD Instinct MI60 32GB with 32 GB of usable memory can run Flux.1 Dev at FP16 natively. Flux is a 12B parameter diffusion transformer that produces high-quality images. You can also run the Schnell variant for faster generation.

What image and video AI models can I run on AMD Instinct MI60 32GB?

AMD Instinct MI60 32GB (32 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. Flux.1 Dev also runs natively for state-of-the-art image quality. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.

Is AMD Instinct MI60 32GB good for AI image generation?

AMD Instinct MI60 32GB is excellent for AI image generation. With 32 GB of usable memory, it runs all major diffusion models including Flux.1, SDXL, and Stable Diffusion 3.5 at full precision. You can generate high-resolution images quickly and even handle video generation models.

Can AMD Instinct MI60 32GB run Qwen 3.5 27B?

Yes, AMD Instinct MI60 32GB with 32 GB of usable memory can run Qwen 3.5 27B at Q4_K_M (~16.5 GB) with ~7 GB headroom for context and runtime. Quality at Q4 is very close to full precision for most tasks. Run it with: ollama run qwen3.5:27b

What is the best quantization for AI models on AMD Instinct MI60 32GB?

With 32 GB on AMD Instinct MI60 32GB, Q4_K_M is the sweet spot for 27B-35B models, Q6_K for 14B models, and Q8_0 for 8B-9B models. The general rule: use the highest quantization that fits with at least 2-3 GB headroom for KV cache and runtime.

For local LLMs on AMD Instinct MI60 32GB, does VRAM matter more than bandwidth?

AMD Instinct MI60 32GB already has strong memory bandwidth, so the next limit is often memory capacity and context headroom rather than raw decode speed. For local LLMs, fit first and bandwidth second is the right mental model.

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