AMD
AMD Instinct MI100 32GB
InstinctDatacenterCDNAPCIe 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.
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 While superseded by the MI200 and MI300 series, it remains a legitimate ROCm platform for AI inference and is available on the used market at reduced prices.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.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
rocm-supporteddatacenter-gradehigh-bandwidthlegacy
Specifications
Compute
FP16184 TFLOPS
INT8368 TOPS
ArchitectureCDNA
Memory
VRAM32 GB
Bandwidth1228 GB/s
General
FamilyInstinct
SegmentDatacenter
InterconnectPCIe 4
Compute PlatformROCM
MSRP$11,500
Key Features
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
For AI Workloads
Strengths
- 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
Considerations
- 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
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
Recommendations by Workload
Qwen 3 30B A3B 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 120.7 tok/s · 102K ctx · llama.cppEST.
Qwen 3.6 27B 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 32.6 tok/s · 187K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
397BTier 100Needs ~247.7 GB
123BTier 100Needs ~81.8 GB
1000BTier 100Needs ~617.8 GB
1000BTier 100Needs ~617.8 GB
1600BTier 100Needs ~867.0 GB
Image & Video Generation
Diffusion Model Compatibility
43 of 52 models can generate images or video on your AMD Instinct MI100 32GB
Upgrade paths
Upgrade from AMD Instinct MI100 32GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
MacBook Pro M1 Max 64GBNext step up
64 GB Unified (+32)
AUnlocks 11 additional models that do not fit on the current setup.Unlocks Qwen 2.5 VL 72B, Llama 3.3 70B, Llama 3.1 70B+8 more
Unlocks 11 additional models that do not fit on the current setup.
~$2,499 MSRP
Radeon PRO W7900 DS 48GBAMD upgrade
48 GB VRAM (+16)
AUnlocks 13 additional models that do not fit on the current setup.Unlocks Qwen 2.5 VL 72B, Qwen3-Coder-Next, Llama 3.3 70B+10 more
Unlocks 13 additional models that do not fit on the current setup.
~$3,999 MSRP
MacBook Pro M3 Max 128GBBest value
128 GB Unified (+96)
BUnlocks 26 additional models that do not fit on the current setup.Unlocks Devstral 2 123B Instruct, Qwen 3.5 122B A10B, Mistral Small 4 119B+23 more
Unlocks 26 additional models that do not fit on the current setup.
~$2,499 MSRP
AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+256)8000 GB/s (+6772)
BUnlocks 39 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+36 more · +105% faster avg
Unlocks 39 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 105%.
~$8,000 MSRP
Frequently Asked Questions
AMD Instinct MI100 32GBCategory AvgMacBook Pro M1 Max 64GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~2.1s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~16.4s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~11.5s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~1.8s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~5.3s/frame |
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 fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 32.6 tok/s · 187K ctx · llama.cppEST.
Devstral Small 2 24B Instruct 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 58.6 tok/s · 87K ctx · llama.cppEST.
Qwen 3.5 27B matches RAG and keeps a practical fit profile. 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, ollama, lm-studio.
Decode 52.3 tok/s · 58K ctx · llama.cppEST.
27B23.7 GB52 tok/s58K ctx
Image
| MAGI-1Video | 256×256 | ~4.9s/frame | F |
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 AMD Instinct MI100 32GB for local AI?
Excellent choice for local AI
Runs 27 of 50 top models well — a strong all-rounder for local inference.
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.
Want more headroom? MacBook Pro M1 Max 64GB (64.0 GB unified memory) is the next step up.
