Graphics cards

Which AI models run on the 2x RTX PRO 6000 Blackwell (192 GB)?

The 2x RTX PRO 6000 Blackwell (192 GB) has 192 GB of memory, about 190 GB of it usable for a model, and 1792 GB/s of memory bandwidth. 105 of the 110 models in our catalog run on it at 4-bit or better.

Data as of 2 November 2026

Compare with other machines

192 GBmemory ~190 GB usable for a model

1792 GB/sbandwidth, sets the speed

105models run at 4-bit or better

Two cards in one PC, model split across both. Each token still reads at one card's bandwidth.

Our pick for this machine DeepSeek-V4-Flash Q4_K_M · 177 of 190 GB · ~125 tok/s our est. The largest model on our shortlist of current models that runs at 4-bit or better and 10 tok/s or more.

Models that run on it

Model	Best build	Memory	Speed
DeepSeek-V4-Flash284B MoE	Q4_K_M	177 GB	~125 tok/s our est.
Qwen3-235B-A22B235B MoE	Q4_K_M	134 GB	~81 tok/s our est.
Qwen3.8-Flash-Next125B MoE	Q4_K_XL	115 GB	~167 tok/s ModelFit est.
Nemotron 3 Super 120B-A12B120B MoE	Q4_K_M	90 GB	~95 tok/s ModelFit est.
Nemotron 3.5 Super VL 120B-A12B120B MoE	Q8_0	132 GB	~77 tok/s our est.
gpt-oss-120b117B MoE	MXFP4	68 GB	~215 tok/s ModelFit est.
Llama 3.3 70B70.6B	Q8_0	78 GB	~21 tok/s ModelFit est.
Llama 3.3 Nemotron Super 49B49B	FP16	101 GB	~10 tok/s our est.
Qwen3.6-35B-A3B35B MoE	Q8_0	41 GB	~234 tok/s ModelFit est.
Gemma 4 31B30.7B	Q8_0	33 GB	~45 tok/s ModelFit est.
Qwen3-Coder-30B-A3B30.5B MoE	Q8_0	34 GB	~193 tok/s ModelFit est.
Nemotron 3 Nano 30B-A3B30B MoE	FP16	62 GB	~164 tok/s our est.
Qwen3.8-27B27.3B	Q8_0	29 GB	~54 tok/s ModelFit est.
Gemma 4 26B-A4B25.2B MoE	Q8_0	30 GB	~184 tok/s ModelFit est.
gpt-oss-20b21B MoE	MXFP4	15.1 GB	~242 tok/s ModelFit est.
Nemotron Nano 9B v29B	FP16	19.4 GB	~55 tok/s our est.
Qwen3.5 9B Instruct9B	Q8_0	12.0 GB	~146 tok/s ModelFit est.
Qwen2.5 7B Instruct7B	Q4_K_M	6.6 GB	~224 tok/s ModelFit est.
Gemma 3 4B Instruct4B	Q8_0	5.0 GB	~232 tok/s ModelFit est.
Ornith 1.5 122B125.09B	Q4_K_M	79 GB	~162 tok/s ModelFit est.
Ling-3.0-flash124B	Q4_K_M	81 GB	~223 tok/s ModelFit est.
Devstral 2 123B123B	Q4_K_M	78 GB	~21 tok/s ModelFit est.
Qwen3.5 122B-A10B Instruct122B MoE	Q4_K_M	75 GB	~154 tok/s ModelFit est.
Laguna S 2.1118B	Q4_K_M	99 GB	~130 tok/s ModelFit est.
Llama 4 Scout109B MoE	Q4_K_M	70 GB	~112 tok/s ModelFit est.
Qwen3-Next 80B-A3B80B MoE	Q8_0	88 GB	~285 tok/s ModelFit est.
Kolibri 178.1B	Q4_K_M	52 GB	~260 tok/s ModelFit est.
Llama 3.1 70B Instruct70B	Q4_K_M	44 GB	~36 tok/s ModelFit est.
DeepSeek-R1 Distill Llama 70B70B	Q4_K_M	44 GB	~36 tok/s ModelFit est.
Mixtral 8x7B Instruct46.7B MoE	Q4_K_M	32 GB	~108 tok/s ModelFit est.
Flint 40B-A2.5B39.8B MoE	NVFP4	24 GB	~327 tok/s ModelFit est.
Sage-A1.535.95B	Q4_K_M	23 GB	~314 tok/s ModelFit est.
Qwen3.5 35B-A3B Instruct35B MoE	Q8_0	41 GB	~234 tok/s ModelFit est.
Ornith 1.0 35B35B	Q4_K_M	23 GB	~317 tok/s ModelFit est.
Nanbeige4.3 33B33.1B	Q8_0	37 GB	~44 tok/s ModelFit est.
Poolside Laguna XS.233B	Q4_K_M	25 GB	no estimate
Laguna XS 2.133B	Q4_K_M	22 GB	~302 tok/s ModelFit est.
Qwen3 32B32B	Q8_0	37 GB	~43 tok/s ModelFit est.
DeepSeek-R1 Distill Qwen 32B32B	Q8_0	37 GB	~43 tok/s ModelFit est.
Olmo 3.1 32B Instruct32B	Q4_K_M	21 GB	~71 tok/s ModelFit est.
Qwen3 30B30B	Q8_0	32 GB	~193 tok/s ModelFit est.
NVIDIA Nemotron Cascade 2 30B-A3B30B MoE	Q6_K	26 GB	~411 tok/s our est.
Cohere North Mini Code30B	Q4_K_M	20 GB	no estimate
Nemotron 3.5 Lightning 30B-A3B30B MoE	Q4_K_M	27 GB	~233 tok/s ModelFit est.
GLM-4.7-Flash 30B-A3B30B MoE	Q4_K_M	20 GB	~252 tok/s ModelFit est.
Granite Switch 4.1 30B30B	Q4_K_M	23 GB	~66 tok/s ModelFit est.
Muse Glimmer 30B29.8B	Q4_K_M	18.3 GB	~88 tok/s ModelFit est.
Granite 4.2 30B29.3B	Q4_K_M	17.9 GB	~77 tok/s ModelFit est.
AREX-2 27B27.4B	Q4_K_M	18.6 GB	~85 tok/s ModelFit est.
Gemma 2 27B Instruct27B	Q4_K_M	23 GB	~78 tok/s ModelFit est.
Gemma 3 27B Instruct27B	Q4_K_M	23 GB	~82 tok/s ModelFit est.
Qwen3.5 27B Instruct27B	Q8_0	29 GB	~54 tok/s ModelFit est.
Qwen3.6 27B27B	Q8_0	32 GB	~54 tok/s ModelFit est.
Jebadiah 27B27B	Q8_0	31 GB	~54 tok/s ModelFit est.
Mistral Small 3.124B	Q8_0	25 GB	~57 tok/s ModelFit est.
LFM2 24B-A2B Instruct24B MoE	Q4_K_M	15.3 GB	~330 tok/s ModelFit est.
Devstral Small 2 24B24B	Q4_K_M	16.6 GB	~90 tok/s ModelFit est.
Mistral Small 22B22B	Q4_K_M	18.4 GB	~102 tok/s ModelFit est.
Qwen2.5 14B Instruct14B	Q4_K_M	12.3 GB	~136 tok/s ModelFit est.
Phi-3 Medium 14B14B	Q4_K_M	12.3 GB	~90 tok/s our est.
Phi-4 14B14B	Q8_0	15.9 GB	~88 tok/s ModelFit est.
Qwen2.5 Coder 14B14B	Q4_K_M	12.3 GB	~136 tok/s ModelFit est.
Qwen3 14B14B	Q8_0	17.3 GB	~89 tok/s ModelFit est.
DeepSeek-R1 Distill Qwen 14B14B	Q8_0	16.0 GB	~89 tok/s ModelFit est.
Ministral 3 14B13.5B	Q4_K_M	10.3 GB	~136 tok/s ModelFit est.
Mellum2.1 12B-A2.5B Thinking12.1B MoE	Q4_K_M	9.3 GB	~589 tok/s our est.
Mistral Nemo 12B12B	Q8_0	13.4 GB	~104 tok/s ModelFit est.
Gemma 3 12B Instruct12B	Q4_K_M	10.7 GB	~154 tok/s ModelFit est.
Gemma 4 12B12B	Q8_0	14.1 GB	~103 tok/s ModelFit est.
GLM-4-9B-Chat9.4B	Q8_0	11.2 GB	~134 tok/s ModelFit est.
Gemma 2 9B Instruct9B	Q4_K_M	8.2 GB	~165 tok/s ModelFit est.
Ornith 1.0 9B9B	Q4_K_M	6.7 GB	~211 tok/s ModelFit est.
MiMo-V2.6-Distill-Qwen-9B9B	Q4_K_M	6.9 GB	~206 tok/s ModelFit est.
Jebadiah 9B9B	Q8_0	11.0 GB	~144 tok/s ModelFit est.
Granite 4.2 8B8.8B	Q4_K_M	6.1 GB	~193 tok/s ModelFit est.
Granite Switch 4.1 8B8.8B	Q4_K_M	7.6 GB	~166 tok/s ModelFit est.
MiniCPM-V 4.5 8B8.7B	Q4_K_M	6.8 GB	~205 tok/s ModelFit est.
Ministral 3 8B8.4B	Q4_K_M	7.1 GB	~183 tok/s ModelFit est.
LFM2.5 8B-A1B8.3B MoE	Q4_K_M	6.6 GB	~374 tok/s ModelFit est.
Llama 3.1 8B Instruct8B	Q8_0	9.2 GB	~146 tok/s ModelFit est.
Qwen3 8B8B	Q8_0	9.3 GB	~142 tok/s ModelFit est.
Granite 4.1 8B Instruct8B	Q4_K_M	6.6 GB	~187 tok/s ModelFit est.
Mistral 7B Instruct7B	Q8_0	8.4 GB	~151 tok/s ModelFit est.
Qwen2.5 Coder 7B7B	Q4_K_M	6.6 GB	~224 tok/s ModelFit est.
DeepSeek-R1 Distill Qwen 7B7B	Q8_0	8.7 GB	~157 tok/s ModelFit est.
Gemma 4 E4B4.5B	Q8_0	8.7 GB	~211 tok/s ModelFit est.
Qwen3.5 4B Instruct4B	Q8_0	5.4 GB	~223 tok/s ModelFit est.
NVIDIA Nemotron 3.5 Bolt E4B4B	Q4_K_M	9.1 GB	~248 tok/s ModelFit est.
Phi-3 Mini 3.8B3.8B	Q4_K_M	4.3 GB	~308 tok/s our est.
Phi-4 Mini 3.8B3.8B	Q8_0	4.9 GB	~223 tok/s ModelFit est.
Granite 4.2 3B3.7B	Q4_K_M	3.2 GB	~318 tok/s ModelFit est.
Ministral 3 3B3.4B	Q4_K_M	4.1 GB	~268 tok/s ModelFit est.
Qwen2.5 3B Instruct3B	Q4_K_M	3.6 GB	~341 tok/s ModelFit est.
Llama 3.2 3B Instruct3B	Q4_K_M	3.6 GB	~314 tok/s ModelFit est.
Granite 4.1 3B Instruct3B	Q4_K_M	3.0 GB	~317 tok/s ModelFit est.
Gemma 4 E2B2.3B	Q8_0	5.7 GB	~341 tok/s ModelFit est.
Gemma 2 2B Instruct2B	Q4_K_M	2.8 GB	~336 tok/s ModelFit est.
Qwen3.5 2B Instruct2B	Q4_K_M	2.8 GB	~343 tok/s ModelFit est.
LFM3 1.7B1.73B	Q4_K_M	2.1 GB	~423 tok/s ModelFit est.
Qwen2.5 1.5B Instruct1.5B	Q4_K_M	2.5 GB	~428 tok/s ModelFit est.
Llama 3.2 1B Instruct1B	Q4_K_M	2.0 GB	~447 tok/s ModelFit est.
Gemma 3 1B Instruct1B	Q8_0	2.0 GB	~429 tok/s ModelFit est.
Qwen3.5 0.8B Instruct0.8B	Q4_K_M	1.8 GB	~457 tok/s ModelFit est.
Qwen2.5 0.5B Instruct0.5B	Q4_K_M	1.8 GB	~517 tok/s ModelFit est.
SmolLM2 360M0.36B	Q4_K_M	1.5 GB	~453 tok/s ModelFit est.

Speeds come from ModelFit's estimates (CC BY 4.0) where it covers the model, otherwise from our bandwidth formula. Method

Fits only at 3-bit or lower

GLM-5.3-Flash.

Too big for this machine

Llama 3.1 405B Instruct (250 GB), Llama 4 Maverick (252 GB), Ling-3.1-flash-1019 (368 GB), DeepSeek-R1 671B (390 GB).

Questions

What is the best AI model for the 2x RTX PRO 6000 Blackwell (192 GB)?

DeepSeek-V4-Flash at Q4_K_M: the largest model on our shortlist of current models that runs at 4-bit or better at a usable speed (about 125 tok/s, our est.).

How much memory can a model use on the 2x RTX PRO 6000 Blackwell (192 GB)?

About 190 GB of the 192 GB. Two cards in one PC, model split across both. Each token still reads at one card's bandwidth.

How fast is the 2x RTX PRO 6000 Blackwell (192 GB) for local LLMs?

Decode speed is limited by memory bandwidth: 1792 GB/s. A model that reads 10 GB per token runs at under 179 tokens per second.