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⇱ AI Models for AMD Instinct MI300X 192GB — What Runs on 192GB VRAM


AMD

AMD Instinct MI300X 192GB

InstinctDatacenterCDNA 3OAMROCm

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 AMD Instinct MI300X 192GB →

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.

CapabilityStatusRepresentative ModelDetail
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Runs nativelyQwen 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

Architecture

CDNA 3

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

Chat

S

Qwen 3.5 122B A10B

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.
121.4 GB / 192.0 GB VRAM

Coding

S

Devstral 2 123B Instruct

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.
126.3 GB / 192.0 GB VRAM

Agentic Coding

Full Model Compatibility

👁 DeepSeek
DeepSeek V4 Flash
S96
284B179.4 GB89 tok/s169K ctx
moe
👁 Alibaba
Qwen 3.5 122B A10B
S95
122B97.0 GB166 tok/s131K ctx
moe
👁 Mistral
Devstral 2 123B Instruct
S

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

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

ModelMax ResolutionGen TimeGrade
SD TurboImage512×5120msS
Stable Diffusion 1.5Image512×768100msS
Realistic Vision v5.1Image512×768100msS
DreamShaper 8Image512×768100msS
LCM DreamShaper v7

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.

ConfigEffective memoryModels that fitEst. bandwidth
1× AMD192 GB359/3745,300 GB/s
2× AMD384 GB366/3749,328 GB/s
4× AMD768 GB373/37418,656 GB/s
8× AMD1536 GB374/37437,312 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

8× AMD Instinct MI300X 192GBMulti-GPU
8 × 192 GB = 1536 GB effectivevia Infinity Fabric (896 GB/s)
B
Unlocks 15 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Kimi K2.5, Kimi K2.6+12 more · +135% faster avg

Unlocks 15 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 135%.

Infinity Fabric gives this scale-out path a cleaner inter-GPU story than PCIe-only builds.

~$15,000 MSRP

AMD Instinct MI325X 256GBNext step up
256 GB VRAM (+64)6000 GB/s (+700)
B
Unlocks 4 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Llama 4 Maverick 17B 128E, Llama 3.1 405B+1 more · +2% faster avg

Unlocks 4 additional models that do not fit on the current setup.

~$20,000 MSRP

AMD Instinct MI350X 288GBBest value
288 GB VRAM (+96)8000 GB/s (+2700)
B
Unlocks 5 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Qwen3-Coder 480B A35B Instruct, Llama 4 Maverick 17B 128E+2 more · +14% faster avg

Unlocks 5 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 14%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

AMD Instinct MI300X 192GB vs NVIDIA GB200 192GBAMD Instinct MI300X 192GB vs B100 192GBAMD Instinct MI300X 192GB vs H100 NVL 188GB
Compare this GPUCompare with another GPU →
192GB
VRAM
5.3kGB/s
Bandwidth
1.3kTFLOPS
FP16 Compute
2.6kTOPS
INT8 Inference
$15,000 MSRP
AMD Instinct MI300X 192GBCategory AvgAMD Instinct MI325X 256GB
Runs natively
Llama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16~200ms per image
Image Gen (Flux)Runs nativelyFlux.1 Dev FP16~~1.1s per image
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16~~1.3s per image
Video Short (25f)Runs nativelyLTX Video 2B~200ms/frame
Video Long (100f)Runs nativelyWan Video 14B~600ms/frame
S

Devstral 2 123B Instruct

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.
131.7 GB / 192.0 GB VRAM

Reasoning

S

Devstral 2 123B Instruct

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.
126.3 GB / 192.0 GB VRAM

RAG

S

Qwen 3.5 122B A10B

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.
125.0 GB / 192.0 GB VRAM
95
123B100.5 GB60 tok/s256K ctx
dense
👁 Mistral
Mistral Small 4 119B
S93
119B98.1 GB180 tok/s256K ctx
moe
👁 Alibaba
Qwen 3 235B A22B
S92
235B166.3 GB84 tok/s131K ctx
moe
👁 OpenAI
GPT-OSS 120B
S92
117B96.4 GB63 tok/s131K ctx
dense
👁 Cohere
Command A 111B
S92
111B91.7 GB67 tok/s262K ctx
dense
👁 Mistral AI
Pixtral Large 124B
S91
124B101.1 GB59 tok/s131K ctx
dense
MiniMax M2.7
S90
230B164.2 GB96 tok/s134K ctx
moe
👁 Alibaba
Qwen 2.5 VL 72B
S90
72B68.9 GB102 tok/s33K ctx
dense
👁 Alibaba
Qwen3-Coder-Next
S90
80B70.4 GB279 tok/s256K ctx
moe
👁 Alibaba
Qwen3-Coder 30B A3B Instruct
S89
30.5B40.2 GB625 tok/s256K ctx
moe
👁 Alibaba
Qwen 3.6 35B A3B
S89
35B45.6 GB525 tok/s262K ctx
+1moe
👁 Mistral
Leanstral 119B A6B
S89
119B101.5 GB166 tok/s181K ctx
moe
👁 Alibaba
Qwen 3.5 27B
S89
27B39.7 GB271 tok/s131K ctx
dense
👁 Alibaba
Qwen3-VL 30B A3B Instruct
S89
30B39.9 GB647 tok/s256K ctx
moe
👁 Alibaba
Qwen 3.6 27B
S89
27B37.5 GB169 tok/s262K ctx
+1dense
👁 Alibaba
Qwen 3.5 35B A3B
S88
35B42.9 GB571 tok/s131K ctx
moe
👁 Alibaba
Qwen 3 32B
S88
32B43.5 GB230 tok/s131K ctx
dense
👁 Mistral
Magistral Small 2507
S87
24B37.2 GB304 tok/s131K ctx
dense
👁 Mistral
Devstral Small 2 24B Instruct
S87
24B37.2 GB304 tok/s256K ctx
dense
👁 Alibaba
Qwen 3 30B A3B
S87
30.5B40.2 GB625 tok/s131K ctx
moe
👁 Alibaba
Qwen 3.5 9B
S87
9B27.8 GB126 tok/s131K ctx
dense
👁 NVIDIA
Nemotron 3 Nano 30B
S87
30B40.8 GB243 tok/s131K ctx
dense
👁 Alibaba
Qwen 3 14B
S86
14B31.1 GB196 tok/s131K ctx
dense
👁 Mistral
Devstral Small 1.1
S86
24B37.2 GB304 tok/s131K ctx
dense
👁 Microsoft
Phi-4-reasoning-plus 14B
S85
14.7B32.1 GB206 tok/s33K ctx
dense
👁 Alibaba
Qwen 3 8B
A85
8B27.2 GB112 tok/s131K ctx
dense
👁 Google
Gemma 4 31B
A85
30.7B53.5 GB144 tok/s167K ctx
dense
👁 OpenAI
GPT-OSS 20B
A84
21B35.4 GB794 tok/s128K ctx
moe
👁 NVIDIA
Nemotron Cascade 2 30B A3B
A84
30B41.3 GB639 tok/s262K ctx
moe
👁 Alibaba
Qwen 3.5 4B
A83
4B24.7 GB56 tok/s131K ctx
dense
👁 LG AI
EXAONE 4.0 32B
A82
32B43.5 GB229 tok/s131K ctx
dense
👁 Google
Gemma 4 26B A4B
A81
25.2B39.1 GB671 tok/s256K ctx
moe
👁 Mistral
Ministral 3 14B
A80
14B31.1 GB196 tok/s262K ctx
multimodal
👁 NVIDIA
Nemotron Nano 8B
A80
8B26.9 GB112 tok/s131K ctx
dense
👁 Microsoft
Phi-4 Mini Reasoning 4B
A80
3.8B23.9 GB53 tok/s131K ctx
dense
👁 DeepSeek
DeepSeek Coder V2 236B
A77
236B222.7 GB43 tok/s8K ctx
moe
👁 Jina AI
Jina Embeddings v3
A74
0.57B23.2 GB8 tok/s8K ctx
dense
👁 BAAI
BGE M3
A73
0.57B22.4 GB8 tok/s8K ctx
dense
👁 Alibaba
Qwen 3.5 397B A17B
F0
397B265.1 GB21 tok/s4K ctx
moe
👁 Moonshot AI
Kimi K2.5
F0
1000B637.5 GB3 tok/s4K ctx
moe
👁 Moonshot AI
Kimi K2.6
F0
1000B637.5 GB3 tok/s4K ctx
+1moe
👁 DeepSeek
DeepSeek V4 Pro
F0
1600B884.0 GB3 tok/s4K ctx
moe
👁 Z.ai
GLM-5.1
F0
754B499.1 GB4 tok/s4K ctx
moe
👁 Z.ai
GLM-5
F0
744B493.0 GB4 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.2
F0
671B429.9 GB5 tok/s4K ctx
moe
👁 Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B315.8 GB11 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek R1 671B
F0
671B489.0 GB4 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.1 671B
F0
671B489.0 GB4 tok/s4K ctx
moe
Image
512×768
0ms
S
PixArt-SigmaImage1024×1024200msS
FramePack I2VVideo1280×720400ms/frameS
SDXL TurboImage512×5120msS
SDXL LightningImage1024×1024100msS
Stable Diffusion XL 1.0Image1024×1024200msS
Playground v2.5Image1024×1024400msS
RealVisXL v5.0Image1024×1024300msS
DreamShaper XLImage1024×1024300msS
Juggernaut XL v9Image1024×1024300msS
Animagine XL 3.1Image1024×1024300msS
Pony Diffusion V6 XLImage1024×1024300msS
Animagine XL 4.0Image1024×1024300msS
Illustrious XLImage1024×1024300msS
Wan Video 2.1 1.3BVideo480×832200ms/frameS
Stable Diffusion 3.5 MediumImage1024×1024400msS
Flux.2 Klein 4BImage1024×1024100msS
LTX Video 2BVideo1280×720200ms/frameS
KolorsImage1024×1024500msS
Stable CascadeImage1024×1024600msS
AuraFlow v0.3Image1536×1536~1.1sS
Stable Diffusion 3.5 LargeImage1024×1024~1.3sS
Stable Diffusion 3.5 Large TurboImage1024×1024200msS
CogVideoX 2BVideo720×480200ms/frameS
HunyuanVideoVideo720×1280400ms/frameS
ChromaImage1024×1024200msS
Z-Image TurboImage1536×1536300msS
Flux.1 DevImage1024×1024~1.1sS
Flux.1 SchnellImage1024×1024200msS
LTX Video 13BVideo1280×720400ms/frameS
Flux.1 Kontext DevImage1024×1024~1.2sS
AnimateDiff v1.5.3Video512×768100ms/frameS
Cosmos Diffusion 7BVideo1024×576400ms/frameS
CogVideoX 5BVideo720×480300ms/frameS
Wan2.2 TI2V 5BVideo832×480300ms/frameS
Flux.2 Klein 9BImage1024×1024100msS
Flux.1 Fill DevImage1024×1024~1sS
Mochi 1 PreviewVideo848×480400ms/frameS
HunyuanVideo 1.5Video720×1280400ms/frameS
Helios 14BVideo1280×720500ms/frameS
SkyReels V2 14BVideo1280×720500ms/frameS
Wan Video 2.1 14BVideo720×1280500ms/frameS
Wan Video 2.2 14BVideo720×1280500ms/frameS
Qwen ImageImage1024×1024400msS
Qwen Image EditImage1024×1024400msS
Flux.2 DevImage1024×1024~11.6sS
MAGI-1Video1280×720600ms/frameS
HunyuanImage 3.0Image1024×1024700msB

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.

192.0 GB

VRAM

$15,000

MSRP

$78/GB

Cost per GB VRAM

Best models for this GPU

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.