AMD
AMD Instinct MI300X 192GB
InstinctDatacenterCDNA 3OAMROCm
Operating mode
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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 MI300X 192GB is AMD's flagship CDNA 3 discrete GPU accelerator, targeting LLM inference and training at scale. With 192 GB of HBM3 memory and 5.3 TB/s of bandwidth, it outspecifies the NVIDIA H100 80GB in raw memory capacity and bandwidth. The 1307 TFLOPS FP16 compute, FP8 support, and full ROCm maturity make it AMD's primary datacenter AI product and the main alternative to NVIDIA in large-scale inference deployments.
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-bandwidthhigh-vramflagship
Specifications
Compute
FP161307 TFLOPS
INT82614 TOPS
ArchitectureCDNA 3
Memory
VRAM192 GB
Bandwidth5300 GB/s
General
FamilyInstinct
SegmentDatacenter
InterconnectOAM
Compute PlatformROCM
MSRP$15,000
Key Features
CDNA 3 architecture (8 × GCD chiplets, OAM form factor)192 GB HBM3 across 8 stacks5.3 TB/s memory bandwidth304 Compute Units with third-generation Matrix Cores (FP8/BF16/FP16)AMD Infinity Fabric xGMI multi-card interconnectFull ROCm support — AMD's premier AI inference platform
For AI Workloads
Strengths
- 192 GB HBM3 enables inference of 405B FP16 models in a single card
- 5.3 TB/s bandwidth far exceeds H100 SXM (3.35 TB/s) for decode throughput
- FP8 matrix cores enable efficient quantized inference at scale
- Mature ROCm support — vLLM, PyTorch ROCm, and SGLang all production-ready
Considerations
- OAM form factor requires specialized server infrastructure
- ROCm software maturity still lags CUDA for cutting-edge research workloads
- Training performance typically behind H100 despite similar inference throughput
- Very high cost — primarily justified for large-scale production inference
CDNA 3 powers the Instinct MI300X (GPU-only, 192 GB HBM3) and MI300A (APU with integrated CPU). It features advanced packaging with up to 12 chiplets and native FP8 support for AI inference.
AI Relevance
The MI300X with 192 GB HBM3 can hold even the largest open-weight models (70B+ at full precision) entirely in GPU memory. FP8 support and mature ROCm stack make it a serious competitor to NVIDIA H100 for AI inference.
Process: TSMC 5nm + 6nmPlatform: ROCMPrecisions: FP64, FP32, TF32, FP16, BF16, FP8, INT8
Recommendations by Workload
Qwen 3.5 122B A10B 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, lm-studio.
Decode 129.8 tok/s · 131K ctx · llama.cppEST.
Devstral 2 123B Instruct 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 46.8 tok/s · 212K 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 ~263.7 GB
Also runs on 2× your GPU via Infinity Fabric — 98 tok/s
1000BTier 100Needs ~633.8 GB
Also runs on 4× your GPU via Infinity Fabric — 79 tok/s
1000BTier 100Needs ~633.8 GB
Also runs on 4× your GPU via Infinity Fabric — 79 tok/s
1600BTier 100Needs ~883.0 GB
Also runs on 8× your GPU via Infinity Fabric — 116 tok/s
754BTier 92Needs ~489.6 GB
Also runs on 4× your GPU via Infinity Fabric — 92 tok/s
Image & Video Generation
Diffusion Model Compatibility
52 of 52 models can generate images or video on your AMD Instinct MI300X 192GB
Multi-GPU scaling
AMD Instinct MI300X 192GB — Up to 8× via Infinity Fabric
Scale out with multiple GPUs for larger models. Infinity Fabric provides 896 GB/s inter-GPU bandwidth with 12% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|
| 1× AMD | 192 GB | 359/374 | 5,300 GB/s |
| 2× AMD | 384 GB | 366/374 | 9,328 GB/s |
| 4× AMD | 768 GB | 373/374 | 18,656 GB/s |
| 8× AMD | 1536 GB | 374/374 | 37,312 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.88× per additional GPU.
Upgrade paths
Upgrade from AMD Instinct MI300X 192GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
AMD Instinct MI300X 192GBCategory AvgAMD Instinct MI325X 256GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~200ms per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~1.1s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~1.3s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~200ms/frame |
| Video Long (100f) | Runs natively | Wan Video 14B | ~600ms/frame |
S
Devstral 2 123B Instruct 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 46.8 tok/s · 212K ctx · llama.cppEST.
Devstral 2 123B 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, lm-studio.
Decode 46.8 tok/s · 212K ctx · llama.cppEST.
Qwen 3.5 122B A10B matches RAG 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, lm-studio.
Decode 129.8 tok/s · 131K ctx · llama.cppEST.
95
123B100.5 GB60 tok/s256K ctx
Image
| MAGI-1Video | 1280×720 | 600ms/frame | S |
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 MI300X 192GB for local AI?
Excellent choice for local AI
Runs 40 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.
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 4 additional models that do not fit on the current setup.
Want more headroom? AMD Instinct MI325X 256GB (256.0 GB VRAM) is the next step up.