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Instinct MI300X

Instinct MI300X has 192 GB of VRAM at 5300 GB/s — about 178.56 GiB usable after driver and compositor overhead. 2108 of 2118 indexed models fit at 128K context with q4_0 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
192 GB
HBM3
Bandwidth
5300 GB/s
8192-bit bus
Tensor FP16
1307 TF
dense
TDP
750 W
Context4K8K16K32K64K128K
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
vision language 192text 1812image 2audio tts 21audio asr 39video 16embedding 26

What fits at 128K context

largest quantization that fits, per model · 2108 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
Ornith-1.0-397BMoEIQ3_M397B176.54 GiB1.05 GiB178.55 GiB0.01 GiB110±37%
Nex-N2-ProMoEQ3_K_L397B176.46 GiB1.05 GiB178.47 GiB0.09 GiB110±37%
Qwen3.5-397B-A17BMoEQ3_K_M403B175.29 GiB1.05 GiB177.29 GiB1.27 GiB111±37%
DeepSeek-V3.1-TerminusMoEUD-IQ1_S685B173.84 GiB2.41 GiB177.23 GiB1.33 GiB94±37%
dots.llm1.instMoEQ8_0143B141.37 GiB34.88 GiB177.18 GiB1.38 GiB37±37%
DeepSeek-V3-0324MoEUD-IQ1_S685B173.45 GiB2.41 GiB176.84 GiB1.72 GiB94±37%
cogito-v2-preview-deepseek-671B-MoEMoEUD-IQ1_S671B173.12 GiB2.41 GiB176.51 GiB2.05 GiB95±37%
cogito-671b-v2.1MoEUD-IQ1_S671B173.11 GiB2.41 GiB176.50 GiB2.06 GiB95±37%
ERNIE-4.5-300B-A47B-PTQ4_K_M300B167.78 GiB7.59 GiB176.40 GiB2.16 GiB19±26.5%
Minimax-M3-abliterated-cleanMoEQ3_K_S427B171.06 GiB4.22 GiB176.20 GiB2.36 GiB88±37%
DeepSeek-R1-0528MoEUD-IQ1_S685B172.75 GiB2.41 GiB176.14 GiB2.42 GiB95±37%
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16MoEUD-IQ1_M561B175.12 GiB0.00 GiB176.05 GiB2.51 GiB96±37%
DeepSeek-TNG-R1T2-ChimeraMoEUD-IQ1_S685B172.39 GiB2.41 GiB175.78 GiB2.78 GiB95±37%
Hy3MoEQ4_K_S299B163.41 GiB11.25 GiB175.59 GiB2.97 GiB65±37%
DeepSeek-Prover-V2-671BMoEUD-IQ1_S685B172.10 GiB2.41 GiB175.49 GiB3.07 GiB95±37%
Qwen3.5-REAP-262B-A17BMoEQ5_K_M262B172.96 GiB1.05 GiB174.96 GiB3.60 GiB102±37%
Step-3.7-FlashQ6_K_L201B160.20 GiB13.79 GiB174.92 GiB3.64 GiB19±26.5%
DeepSeek-V3.2MoEUD-IQ1_S685B171.48 GiB2.41 GiB174.87 GiB3.69 GiB95±37%
MiMo-V2.5MoEKV unresolvedQ4_K_S311B169.41 GiB4.22 GiB174.58 GiB3.98 GiB88±37%
GLM-4.5MoEQ3_K_M358B159.51 GiB12.94 GiB173.38 GiB5.18 GiB60±37%
GLM-4.7MoEQ3_K_M358B159.51 GiB12.94 GiB173.38 GiB5.18 GiB60±37%
GLM-5.2MoEQ3_K_M753B169.33 GiB3.08 GiB173.36 GiB5.20 GiB93±37%
Solar-Open2-250BMoEQ5_K_M250B165.65 GiB6.75 GiB173.32 GiB5.24 GiB84±37%
MiMo-V2-FlashMoEKV unresolvedQ4_K_S310B168.15 GiB4.22 GiB173.32 GiB5.24 GiB89±37%
Trinity-Large-PreviewMoEUD-IQ3_XXS399B170.01 GiB2.33 GiB173.27 GiB5.29 GiB110±37%
MiniMax-M3MoEIQ3_XXS427B167.65 GiB4.22 GiB172.78 GiB5.78 GiB89±37%
GLM-4.6MoEQ3_K_M357B158.89 GiB12.94 GiB172.76 GiB5.80 GiB60±37%
Qwen3-Coder-480B-A35B-InstructMoEQ2_K_L480B162.87 GiB8.72 GiB172.52 GiB6.04 GiB68±37%
Trinity-Large-ThinkingMoEQ3_K_M399B168.90 GiB2.33 GiB172.16 GiB6.40 GiB110±37%
GLM-4.6-Derestricted-v3MoEQ3_K_L357B158.25 GiB12.94 GiB172.13 GiB6.43 GiB60±37%
Trinity-Large-TrueBaseMoEQ3_K_M399B168.74 GiB2.33 GiB172.00 GiB6.56 GiB110±37%
Qwen3-Coder-REAP-363B-A35BMoEQ3_K_M363B161.57 GiB8.72 GiB171.23 GiB7.33 GiB63±37%
GLM-4.6-REAP-268B-A32BMoEQ4_1269B156.99 GiB12.94 GiB170.87 GiB7.69 GiB56±37%
Llama-4-Maverick-17B-128E-InstructMoEKV unresolvedQ3_K_S402B160.80 GiB6.75 GiB168.47 GiB10.09 GiB96±37%
GLM-5MoEUD-TQ1_0754B164.05 GiB3.08 GiB168.08 GiB10.48 GiB95±37%
GLM-5.1MoEIQ1_M754B163.93 GiB3.08 GiB167.97 GiB10.59 GiB95±37%
grok-2MoEQ4_1270B157.54 GiB9.00 GiB167.58 GiB10.98 GiB34±37%
MiniMax-M2.7MoEUD-Q5_K_M229B157.23 GiB8.72 GiB166.84 GiB11.72 GiB77±37%
r1-1776MoEIQ2_XXS671B162.45 GiB2.41 GiB165.84 GiB12.72 GiB99±37%
DeepSeek-R1MoEIQ2_XXS685B162.45 GiB2.41 GiB165.84 GiB12.72 GiB99±37%
step-3.5-flashQ6_K199B150.81 GiB13.79 GiB165.53 GiB13.03 GiB20±26.5%
Hermes-3-Llama-3.1-405BIQ3_XXS406B145.14 GiB17.72 GiB164.04 GiB14.52 GiB21±26.5%
Qwen3-235B-A22B-Thinking-2507MoEQ5_K_M235B155.43 GiB6.61 GiB162.97 GiB15.59 GiB70±37%
Qwen3-235B-A22B-Instruct-2507MoEQ5_K_M235B155.43 GiB6.61 GiB162.97 GiB15.59 GiB70±37%
Qwen3-VL-235B-A22B-ThinkingMoEQ5_K_M236B155.36 GiB6.61 GiB162.90 GiB15.66 GiB70±37%
Qwen3-VL-235B-A22B-InstructMoEQ5_K_M236B155.36 GiB6.61 GiB162.90 GiB15.66 GiB70±37%
Qwen3-235B-A22BMoEQ5_K_M235B155.36 GiB6.61 GiB162.90 GiB15.66 GiB70±37%
Qwen3-235B-A22B-abliteratedMoEI1-Q5_K_M235B155.36 GiB6.61 GiB162.90 GiB15.66 GiB70±37%
DeepSeek-V3.1MoEUD-TQ1_0685B158.79 GiB2.41 GiB162.18 GiB16.38 GiB101±37%
MiniMax-M2.1MoEQ5_K_M229B151.23 GiB8.72 GiB160.83 GiB17.73 GiB78±37%
MiniMax-M2MoEQ5_K_M229B151.23 GiB8.72 GiB160.83 GiB17.73 GiB78±37%
MiniMax-M2.5MoEQ5_K_M229B151.16 GiB8.72 GiB160.76 GiB17.80 GiB78±37%
MiniMax-M2.7-BF16-ultra-uncensored-hereticMoEQ5_K_M229B151.16 GiB8.72 GiB160.76 GiB17.80 GiB78±37%
GLM-4.7-REAP-218B-A32BMoEQ5_K_M218B145.23 GiB12.94 GiB159.11 GiB19.45 GiB55±37%
DeepSeek-Coder-V2-Instruct-0724MoEQ5_K236B155.74 GiB2.37 GiB159.05 GiB19.51 GiB98±37%
DeepSeek-V2.5MoEQ5_K236B155.74 GiB2.37 GiB159.05 GiB19.51 GiB98±37%
DeepSeek-Coder-V2-InstructMoEQ5_K_M236B155.74 GiB2.37 GiB159.05 GiB19.51 GiB98±37%
Hermes-4-405BQ2_K406B139.07 GiB17.72 GiB157.97 GiB20.59 GiB21±26.5%
Hunyuan-A13B-InstructMoEBF1680.4B149.76 GiB4.50 GiB155.16 GiB23.40 GiB22±26.5%
DeepSeek-V4-FlashMoEQ4_K291B153.33 GiB0.02 GiB154.29 GiB24.27 GiB127±37%
●From the file◐Predictedwhat these mean

Speed is modeled, not measured: decode is memory-bandwidth bound, so tokens per second is bytes read per token against achievable bandwidth. Mixture-of-experts models carry a wider band because only the routed experts are read each step, and few have been measured publicly.

Measured on this card

third-party benchmarks, aggregated
Workload◍MedianMiddle 50%Runs
Prompt processing11021.13 tok/s4938.37–11679.3611
Text generation169.73 tok/s159.80–225.9011
Benchmarked· n=11

Aggregated from community-submitted runs, so the spread is wide by nature — it covers different models, resolutions, step counts and settings, not one controlled configuration. Read the middle 50% rather than the median alone. These figures are reproduced with attribution from llama.cpp-discussion-14640.

Questions people ask

What AI models can a Instinct MI300X run?
2108 of 2118 indexed open-weight models fit a Instinct MI300X at 131,072 context with q4_0 KV cache, the largest being Ornith-1.0-397B at IQ3_M. That covers text, vision-language, image, video and speech models.
How much usable memory does a Instinct MI300X actually have?
Its nameplate is 192 GB, but about 178.56 GiB is available to a model once driver and compositor overhead is accounted for.
Is a Instinct MI300X fast for local AI?
Its memory bandwidth is 5300 GB/s, and that figure — not teraflops — is what governs token generation speed. Capacity decides what you can run; bandwidth decides how fast it runs.