AMD · workstation

Radeon Pro W7600

Radeon Pro W7600 has 8 GB of VRAM at 288 GB/s — about 7.44 GiB usable after driver and compositor overhead. 974 of 2118 indexed models fit at 32K context with f16 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
8 GB
GDDR6
Bandwidth
288 GB/s
128-bit bus
Tensor FP16
dense
TDP
130 W
$599 MSRP
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 795audio asr 38vision language 88embedding 25audio tts 20video 7image 1

What fits at 32K context

largest quantization that fits, per model · 974 of 2118 indexed
ModelBest quantParamsWeightsKVTotal in memoryHeadroomtok/s
MiniCPM-o-2_6Q4_K_L8.7B4.74 GiB1.75 GiB7.44 GiB0.00 GiB28±26.5%
Arcee-Maestro-7B-PreviewQ4_K_L7.6B4.74 GiB1.75 GiB7.44 GiB0.00 GiB28±26.5%
Jan-v3-4B-base-instructQ2_K_L4.4B2.03 GiB4.50 GiB7.44 GiB0.00 GiB28±26.5%
Jan-code-4bQ2_K_L4.4B2.03 GiB4.50 GiB7.44 GiB0.00 GiB28±26.5%
Voxtral-Mini-3B-2507Q4_K_M4.7B2.78 GiB3.75 GiB7.44 GiB0.00 GiB28±26.5%
Teuken-7B-instruct-research-v0.4Q5_K_L7.5B5.50 GiB1.00 GiB7.44 GiB0.00 GiB28±26.5%
Ministral-3-8B-Instruct-2512UD-IQ1_M8.9B2.25 GiB4.25 GiB7.44 GiB0.00 GiB28±26.5%
Marco-Nano-InstructMoEI1-IQ2_M8.0B3.06 GiB3.50 GiB7.43 GiB0.01 GiB27±37%
Ministral-3-8B-Reasoning-2512UD-IQ1_M8.9B2.24 GiB4.25 GiB7.43 GiB0.01 GiB28±26.5%
gemma-3-12b-it-vl-Gemini-3-Pro-Preview-Heretic-Uncensored-ThinkingI1-IQ2_M12.2B4.01 GiB2.47 GiB7.43 GiB0.01 GiB28±26.5%
gemma-3-12b-it-vl-Deepseek-v3.1-Heretic-Uncensored-ThinkingI1-IQ2_M12.2B4.01 GiB2.47 GiB7.43 GiB0.01 GiB28±26.5%
gemma-3-12b-it-vl-GLM-4.7-Flash-Heretic-Uncensored-ThinkingI1-IQ2_M12.2B4.01 GiB2.47 GiB7.43 GiB0.01 GiB28±26.5%
Floppa-12B-Gemma3-UncensoredI1-IQ2_M12.2B4.01 GiB2.47 GiB7.43 GiB0.01 GiB28±26.5%
gemma-3-12b-it-hereticI1-IQ2_M12.2B4.01 GiB2.47 GiB7.43 GiB0.01 GiB28±26.5%
gemma-3-12b-it-abliteratedIQ2_M12.2B4.01 GiB2.47 GiB7.43 GiB0.01 GiB28±26.5%
deepseek-coder-1.3b-instructI1-IQ3_XXS1.3B0.54 GiB6.00 GiB7.43 GiB0.01 GiB28±26.5%
deepseek-coder-1.3b-baseIQ3_XXS1.3B0.54 GiB6.00 GiB7.43 GiB0.01 GiB28±26.5%
nomic-embed-codeQ5_K_M7.1B4.72 GiB1.75 GiB7.43 GiB0.01 GiB28±26.5%
MiMo-VL-7B-RLUD-IQ1_S8.3B1.99 GiB4.50 GiB7.42 GiB0.02 GiB28±26.5%
DeepSeek-Coder-V2-Lite-BaseMoEI1-IQ2_XS15.7B5.56 GiB0.95 GiB7.42 GiB0.02 GiB55±37%
DeepSeek-Coder-V2-Lite-InstructMoEIQ2_XS15.7B5.56 GiB0.95 GiB7.42 GiB0.02 GiB55±37%
DeepSeek-V2-Lite-ChatMoEIQ2_XS15.7B5.56 GiB0.95 GiB7.42 GiB0.02 GiB55±37%
Wan2.1-T2V-1.3BQ4_01.4B6.50 GiB0.00 GiB7.42 GiB0.02 GiB28±26.5%
internlm3-8b-instructQ4_K_M8.8B4.99 GiB1.50 GiB7.41 GiB0.03 GiB28±26.5%
Yi-Coder-1.5B-ChatIQ2_XS1.5B0.53 GiB6.00 GiB7.41 GiB0.03 GiB28±26.5%
Yi-Coder-1.5BIQ2_XS1.5B0.53 GiB6.00 GiB7.41 GiB0.03 GiB28±26.5%
Kuwutu-7B-CYOA-v2I1-IQ1_M7.6B1.98 GiB4.50 GiB7.41 GiB0.03 GiB28±26.5%
legitus-instruct-v1I1-IQ2_XS8.1B2.44 GiB4.00 GiB7.41 GiB0.03 GiB28±26.5%
Apertus-8B-Instruct-2509I1-IQ2_XS8.1B2.44 GiB4.00 GiB7.41 GiB0.03 GiB28±26.5%
Llama-3.1-Minitron-4B-Width-BaseIQ4_NL4.5B2.49 GiB4.00 GiB7.40 GiB0.04 GiB28±26.5%
next-8bI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Supertron2-Reranker-8BI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
next-ocrI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen3-VL-8B-GLM-4.7-Flash-Heretic-Uncensored-ThinkingI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Midas-FableAgent-8BI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen3-VL-8B-Heretic-1.3.0I1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen3-VL-8B-Thinking-Unredacted-MAXI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen3-VL-8B-Instruct-Minecraft-MT-en-zhI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen-3-VL-8B-Instruct-hereticI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Poe-8B-GLM5-Opus4.6-Sonnet4.5-Kimi-Grok-Gemini-3-pro-preview-HERETICI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
ToolCUA-8BI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Huihui-Qwen3-VL-8B-Instruct-abliteratedI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen3-VL-Reranker-8BI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Salience-1-9BI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Qwen3-VL-8B-Instruct-Uncensored-V2I1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Maestro1-9BI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
GRaPE-2-FlashI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Jan-v2-VL-medI1-IQ1_S8.8B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Parable-Qwen3-8B-Claude-Fable-5I1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
ReasonCritic-7BI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
mythos-9b-unhinged-hereticI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Finch-8B-KTOI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Finch-8BI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
MathSmith-hc-Qwen3-8BI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
MiroThinker-v1.0-8BI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
mythos-9b-unhingedI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Ektome-Qwen3-8B-PristinelyUncensoredI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
Marco-DeepResearch-8BI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
mythos-9b-mergedI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
qwen3-8b-apostateI1-IQ1_S8.2B1.97 GiB4.50 GiB7.40 GiB0.04 GiB28±26.5%
From the filePredictedwhat 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.

Questions people ask

What AI models can a Radeon Pro W7600 run?
974 of 2118 indexed open-weight models fit a Radeon Pro W7600 at 32,768 context with f16 KV cache, the largest being MiniCPM-o-2_6 at Q4_K_L. That covers text, vision-language, image, video and speech models.
How much usable memory does a Radeon Pro W7600 actually have?
Its nameplate is 8 GB, but about 7.44 GiB is available to a model once driver and compositor overhead is accounted for.
Is a Radeon Pro W7600 fast for local AI?
Its memory bandwidth is 288 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.