AMD

AMD Instinct MI100 32GB

InstinctDatacenterCDNAPCIe 4ROCm
32GB
VRAM
1.2kGB/s
Bandwidth
184TFLOPS
FP16 Compute
368TOPS
INT8 Inference
$11,500 MSRP
VRAM32 GBBandwidth1.2k GB/sCompute184 TFInference368 TOPSValue1.6 TF/$k
AMD Instinct MI100 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 MI100 32GB was AMD's first CDNA-architecture accelerator, a significant step forward from Vega for HPC and AI workloads. It features 32 GB of HBM2 with 1.2 TB/s of bandwidth and full ROCm support. While superseded by the MI200 and MI300 series, it remains a legitimate ROCm platform for AI inference and is available on the used market at reduced prices. Its Matrix Core units accelerate FP16 and BF16 operations.

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-gradehigh-bandwidthlegacy

Spezifikationen

Rechenleistung
FP16184 TFLOPS
INT8368 TOPS
ArchitekturCDNA
Speicher
VRAM32 GB
Bandbreite1228 GB/s
Allgemein
FamilieInstinct
SegmentDatacenter
InterconnectPCIe 4
Compute-PlattformROCM
MSRP$11,500

Hauptmerkmale

CDNA architecture (first generation) — compute-focused, no display output32 GB HBM2 on a 4096-bit bus1.2 TB/s memory bandwidth120 Compute Units with Matrix Core accelerationFull ROCm support — official Instinct datacenter cardPCIe Gen 4 x16

Für KI-Workloads

Stärken
  • Full ROCm support — PyTorch, TensorFlow, llama.cpp ROCm all work natively
  • 1.2 TB/s HBM2 bandwidth excels for memory-bandwidth-bound inference
  • 32 GB HBM2 enables 34B Q4 and 13B FP16 inference
  • CDNA Matrix Cores accelerate FP16/BF16 transformer operations
Hinweise
  • 184 TFLOPS FP16 is modest vs newer MI-series — prefill throughput is limited
  • PCIe-only (no Infinity Fabric interconnect) — no multi-GPU ROCm scaling
  • Power hungry (300W) for its compute level
  • Being phased out of active ROCm support as newer generations take priority

Architecture

CDNA

CDNA is AMD's first compute-focused datacenter GPU architecture, splitting from the gaming-oriented RDNA line. The Instinct MI100 introduced Matrix Cores for accelerated matrix operations.

AI Relevance

Matrix Cores provide hardware-accelerated FP16/BF16 compute for AI training and inference. Full ROCm support makes CDNA GPUs viable for production AI workloads, though the ecosystem lags behind NVIDIA CUDA.

Process: TSMC 7nmPlatform: ROCMPrecisions: FP64, FP32, FP16, BF16, INT8

Kaufberatung

Sollten Sie AMD Instinct MI100 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

$11,500

UVP

$359/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 120.7 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 32.6 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 32.6 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 58.6 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 52.3 tok/s · 58K ctx · llama.cppEST.
26.9 GB / 32.0 GB VRAM

Full Model Compatibility

AlibabaQwen3-Coder 30B A3B Instruct
S100
30.5B24.2 GB121 tok/s102K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
S99
30B23.9 GB125 tok/s105K ctx
moe
AlibabaQwen 3.5 27B
S98
27B23.7 GB52 tok/s58K ctx
dense
AlibabaQwen 3 30B A3B
S97
30.5B24.2 GB121 tok/s102K ctx
moe
AlibabaQwen 3.6 35B A3B
S96
35B29.6 GB101 tok/s26K ctx
+1moe
MistralMagistral Small 2507
S96
24B21.2 GB59 tok/s87K ctx
dense
MistralDevstral Small 2 24B Instruct
S96
24B21.2 GB59 tok/s87K ctx
dense
AlibabaQwen 3.5 35B A3B
S95
35B26.9 GB110 tok/s72K ctx
moe
AlibabaQwen 3.6 27B
S95
27B21.5 GB33 tok/s187K ctx
+1dense
NVIDIANemotron 3 Nano 30B
S95
30B24.8 GB47 tok/s63K ctx
dense
NVIDIANemotron Cascade 2 30B A3B
S95
30B25.3 GB123 tok/s52K ctx
moe
MistralDevstral Small 1.1
S94
24B21.2 GB59 tok/s87K ctx
dense
OpenAIGPT-OSS 20B
S93
21B19.4 GB153 tok/s99K ctx
moe
AlibabaQwen 3 14B
S93
14B15.1 GB101 tok/s127K ctx
dense
AlibabaQwen 3 32B
S93
32B27.5 GB45 tok/s34K ctx
dense
MicrosoftPhi-4-reasoning-plus 14B
S92
14.7B16.1 GB96 tok/s33K ctx
dense
GoogleGemma 4 26B A4B
S92
25.2B23.1 GB130 tok/s55K ctx
moe
AlibabaQwen 3.5 9B
S91
9B11.8 GB126 tok/s131K ctx
dense
AlibabaQwen 3 8B
S89
8B11.2 GB112 tok/s131K ctx
dense
MistralMinistral 3 14B
S87
14B15.1 GB101 tok/s127K ctx
multimodal
LG AIEXAONE 4.0 32B
S87
32B27.5 GB44 tok/s34K ctx
dense
AlibabaQwen 3.5 4B
S86
4B8.7 GB56 tok/s131K ctx
dense
NVIDIANemotron Nano 8B
A84
8B10.9 GB112 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
GoogleGemma 4 31B
A75
30.7B37.5 GB15 tok/s10K ctx
dense
BAAIBGE M3
A74
0.57B6.4 GB8 tok/s8K 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 GB5 tok/s4K ctx
moe
DeepSeekDeepSeek V4 Flash
F0
284B163.4 GB3 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B82.1 GB5 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B75.7 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B52.9 GB5 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B80.4 GB2 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B54.4 GB13 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 GB3 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B85.5 GB5 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 MI100 32GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512300msS
Stable Diffusion 1.5Image512×768500msS
Realistic Vision v5.1Image512×768500msS
DreamShaper 8Image512×768500msS
LCM DreamShaper v7Image512×768200msS
PixArt-SigmaImage1024×1024~2.1sS
FramePack I2VVideo256×256~3.8s/frameS
SDXL TurboImage512×512300msS
SDXL LightningImage1024×1024800msS
Stable Diffusion XL 1.0Image1024×1024~2.1sS
Playground v2.5Image1024×1024~3.1sS
RealVisXL v5.0Image1024×1024~2.3sS
DreamShaper XLImage1024×1024~2.3sS
Juggernaut XL v9Image1024×1024~2.3sS
Animagine XL 3.1Image1024×1024~2.3sS
Pony Diffusion V6 XLImage1024×1024~2.3sS
Animagine XL 4.0Image1024×1024~2.3sS
Illustrious XLImage1024×1024~2.3sS
Wan Video 2.1 1.3BVideo480×832~1.5s/frameS
Stable Diffusion 3.5 MediumImage1024×1024~3.6sS
Flux.2 Klein 4BImage1024×1024600msS
LTX Video 2BVideo1280×720~1.8s/frameS
KolorsImage1024×1024~4.2sS
Stable CascadeImage1024×1024~5.2sS
AuraFlow v0.3Image1536×1536~9.4sS
Stable Diffusion 3.5 LargeImage1024×1024~11.5sS
Stable Diffusion 3.5 Large TurboImage1024×1024~2.1sS
CogVideoX 2BVideo720×480~1.8s/frameS
HunyuanVideoVideo256×256~3.8s/frameS
ChromaImage1024×1024~2.1sS
Z-Image TurboImage1536×1536~2.2sS
Flux.1 DevImage256×256~16.4sS
Flux.1 SchnellImage256×256~3.2sS
LTX Video 13BVideo256×256~3.8s/frameS
Flux.1 Kontext DevImage256×256~18.2sS
AnimateDiff v1.5.3Video512×768~1s/frameS
Cosmos Diffusion 7BVideo1024×576~3s/frameA
CogVideoX 5BVideo720×480~2.6s/frameA
Wan2.2 TI2V 5BVideo832×480~2.6s/frameA
Flux.2 Klein 9BImage1024×1024~1sA
Flux.1 Fill DevImage256×256~15.5sB
Mochi 1 PreviewVideo256×256~6.2s/frameD
HunyuanVideo 1.5Video256×256~6s/frameD
Helios 14BVideo256×256~3.9s/frameF
SkyReels V2 14BVideo256×256~3.9s/frameF
Wan Video 2.1 14BVideo256×256~3.9s/frameF
Wan Video 2.2 14BVideo256×256~3.9s/frameF
Qwen ImageImage256×256~3.5sF
Qwen Image EditImage256×256~3.5sF
Flux.2 DevImage256×256~1m 39sF
MAGI-1Video256×256~4.9s/frameF
HunyuanImage 3.0Image256×256~6.2sF

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 MI100 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 MI100 32GB?

AMD Instinct MI100 32GB (32 GB VRAM) can run these top models: Qwen3-Coder 30B A3B Instruct (score: 100/100), Qwen3-VL 30B A3B Instruct (score: 99/100), Qwen 3.5 27B (score: 98/100). See the full compatibility list above.

How much VRAM does AMD Instinct MI100 32GB have for AI?

AMD Instinct MI100 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 MI100 32GB good for running LLMs locally?

Yes, AMD Instinct MI100 32GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for AMD Instinct MI100 32GB for coding?

For coding on AMD Instinct MI100 32GB, we recommend Qwen 3.6 27B. It achieves 32.6 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 MI100 32GB?

There are 4 upgrade path(s) from AMD Instinct MI100 32GB: MacBook Pro M1 Max 64GB, Radeon PRO W7900 DS 48GB. Upgrading would unlock larger models and faster inference speeds.

Can AMD Instinct MI100 32GB run Flux for image generation?

Yes, AMD Instinct MI100 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 MI100 32GB?

AMD Instinct MI100 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 MI100 32GB good for AI image generation?

AMD Instinct MI100 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 MI100 32GB run Qwen 3.5 27B?

Yes, AMD Instinct MI100 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 MI100 32GB?

With 32 GB on AMD Instinct MI100 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 MI100 32GB, does VRAM matter more than bandwidth?

AMD Instinct MI100 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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