NVIDIA · consumer

GeForce RTX 3060 OEM

GeForce RTX 3060 OEM has 6 GB of VRAM at 336 GB/s — about 5.58 GiB usable after driver and compositor overhead. 1115 of 2118 indexed models fit at 8K context with f16 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
6 GB
GDDR6
Bandwidth
336 GB/s
192-bit bus
Tensor FP16
57 TF
dense
TDP
185 W
Context4K8K16K32K64K128K
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 941vision language 88audio asr 38audio tts 19embedding 26video 3

What fits at 8K context

largest quantization that fits, per model · 1115 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
Crow-9B-HERETIC-4.6I1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKINGI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSOREDI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-Claude-4.6-OS-HERETIC-UNCENSORED-INSTRUCTI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-Claude-4.6-HighIQ-THINKING-HERETIC-UNCENSOREDI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
NaNovel-9BI1-Q3_K_L9.7B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-Unredacted-MAXI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-abliteratedI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Ken3.5-9BI1-Q3_K_L9.7B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Huihui-Qwen3.5-9B-abliteratedQ3_K_L9.7B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-abliteratedQ3_K_L9.0B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-BaseQ3_K_L9.7B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
Qwen3.5-9B-gemini-3.1-opus-4.6-reasoningI1-Q3_K_L9.4B4.49 GiB0.25 GiB5.58 GiB0.00 GiB50±12.9%
MARTHA-LXVII.8B_QWEN-3.5-3.6_prune_9b-3.6_baseI1-Q4_K_S8.1B4.52 GiB0.22 GiB5.58 GiB0.00 GiB50±12.9%
Luna-7B-A4BMoEI1-Q4_K_S6.7B3.64 GiB1.13 GiB5.58 GiB0.00 GiB45±37%
Bonsai-8B-unpackedQ3_K_S8.2B3.62 GiB1.13 GiB5.58 GiB0.00 GiB50±12.9%
gemma-4-E2B-itQ8_05.1B4.70 GiB0.08 GiB5.58 GiB0.00 GiB50±12.9%
gemma-4-E2B-itQ8_05.1B4.70 GiB0.08 GiB5.58 GiB0.00 GiB50±12.9%
canary-qwen-2.5bBF162.6B4.73 GiB0.00 GiB5.57 GiB0.01 GiB50±12.9%
EXAONE-Deep-7.8BQ4_K_L7.8B4.73 GiB0.00 GiB5.57 GiB0.01 GiB50±12.9%
EXAONE-3.5-7.8B-InstructQ4_K_L7.8B4.73 GiB0.00 GiB5.57 GiB0.01 GiB50±12.9%
Apriel-1.6-15b-ThinkerI1-IQ1_S14.9B3.22 GiB1.50 GiB5.57 GiB0.01 GiB50±12.9%
Qwen3-TTS-12Hz-0.6B-BaseQ4_K_M915M4.72 GiB0.00 GiB5.57 GiB0.01 GiB50±12.9%
VoxCPM2F162.3B4.72 GiB0.00 GiB5.57 GiB0.01 GiB50±12.9%
Mistral-7B-v0.1KV unresolvedQ2_K7.2B3.73 GiB1.00 GiB5.57 GiB0.01 GiB50±12.9%
Marco-Nano-InstructMoEI1-Q3_K_M8.0B3.92 GiB0.88 GiB5.57 GiB0.01 GiB105±37%
t5-v1_1-xxlQ2_K4.8B4.72 GiB0.00 GiB5.56 GiB0.02 GiB50±12.9%
granite-3.3-8b-instructIQ3_M8.2B3.48 GiB1.25 GiB5.56 GiB0.02 GiB50±12.9%
granite-3.2-8b-instructIQ3_M8.2B3.48 GiB1.25 GiB5.56 GiB0.02 GiB50±12.9%
Qwythos-9B-v2Q3_K_S9.7B4.48 GiB0.25 GiB5.56 GiB0.02 GiB50±12.9%
Tess-4-9BQ3_K_S9.7B4.48 GiB0.25 GiB5.56 GiB0.02 GiB50±12.9%
Aya-Medikal-V2I1-IQ3_M8.0B3.72 GiB1.00 GiB5.56 GiB0.02 GiB50±12.9%
LFM2.5-8B-A1BMoEUD-Q4_K_S8.5B4.67 GiB0.09 GiB5.56 GiB0.02 GiB137±37%
gemma-3-12b-it-vl-Gemini-3-Pro-Preview-Heretic-Uncensored-ThinkingI1-IQ2_S12.2B3.74 GiB0.97 GiB5.56 GiB0.02 GiB50±12.9%
gemma-3-12b-it-vl-Deepseek-v3.1-Heretic-Uncensored-ThinkingI1-IQ2_S12.2B3.74 GiB0.97 GiB5.56 GiB0.02 GiB50±12.9%
gemma-3-12b-it-vl-GLM-4.7-Flash-Heretic-Uncensored-ThinkingI1-IQ2_S12.2B3.74 GiB0.97 GiB5.56 GiB0.02 GiB50±12.9%
Floppa-12B-Gemma3-UncensoredI1-IQ2_S12.2B3.74 GiB0.97 GiB5.56 GiB0.02 GiB50±12.9%
gemma-3-12b-it-hereticI1-IQ2_S12.2B3.74 GiB0.97 GiB5.56 GiB0.02 GiB50±12.9%
gemma-3-12b-it-abliteratedIQ2_S12.2B3.74 GiB0.97 GiB5.56 GiB0.02 GiB50±12.9%
gemma-4-E2B-it-ultra-uncensored-hereticQ8_05.1B4.68 GiB0.08 GiB5.56 GiB0.02 GiB50±12.9%
Gemma-4-E4B-LuchadorQ3_K_M8.0B4.56 GiB0.18 GiB5.56 GiB0.02 GiB50±12.9%
Phi-3.5-mini-instructQ3_K3.8B1.75 GiB3.00 GiB5.55 GiB0.03 GiB50±12.9%
Tini-Cybersec-8B-A1BMoEQ4_K_S8.5B4.66 GiB0.09 GiB5.55 GiB0.03 GiB137±37%
Qwen3-VL-Embedding-8BQ3_K_M8.1B3.59 GiB1.13 GiB5.55 GiB0.03 GiB50±12.9%
qwen-indic-v1I1-Q3_K_M7.6B3.59 GiB1.13 GiB5.55 GiB0.03 GiB50±12.9%
Qwen3-Embedding-8BQ3_K_M7.6B3.59 GiB1.13 GiB5.55 GiB0.03 GiB50±12.9%
GLM-4.6V-FlashIQ3_M10.3B4.40 GiB0.31 GiB5.55 GiB0.03 GiB50±12.9%
glm4.1v-9b-base-sftI1-IQ3_M10.3B4.40 GiB0.31 GiB5.55 GiB0.03 GiB50±12.9%
GLM-Z1-9B-0414IQ3_M9.4B4.40 GiB0.31 GiB5.55 GiB0.03 GiB50±12.9%
GLM-4-9B-0414IQ3_M9.4B4.40 GiB0.31 GiB5.55 GiB0.03 GiB50±12.9%
Falcon3-7B-InstructIQ4_XS7.5B3.80 GiB0.88 GiB5.54 GiB0.04 GiB51±12.9%
MiMo-VL-7B-RLI1-Q3_K_M8.3B3.59 GiB1.13 GiB5.54 GiB0.04 GiB50±12.9%
Kuwutu-7B-CYOA-v2I1-Q3_K_M7.6B3.59 GiB1.13 GiB5.54 GiB0.04 GiB50±12.9%
Huihui-gemma-3n-E4B-it-abliteratedQ5_K_S7.8B4.54 GiB0.16 GiB5.54 GiB0.04 GiB51±12.9%
NuExtract-1.5IQ3_M3.8B1.73 GiB3.00 GiB5.53 GiB0.05 GiB50±12.9%
Phi-3.5-mini-instructIQ3_M3.8B1.73 GiB3.00 GiB5.53 GiB0.05 GiB50±12.9%
Phi-3.5-mini-instruct_UncensoredIQ3_M3.8B1.73 GiB3.00 GiB5.53 GiB0.05 GiB50±12.9%
Phi-3-mini-128k-instructIQ3_M3.8B1.73 GiB3.00 GiB5.53 GiB0.05 GiB50±12.9%
Phi-3-mini-4k-instructIQ3_M3.8B1.73 GiB3.00 GiB5.53 GiB0.05 GiB50±12.9%
Falcon3-10B-InstructI1-Q2_K_S10.3B3.42 GiB1.25 GiB5.53 GiB0.05 GiB51±12.9%
●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.

Questions people ask

What AI models can a GeForce RTX 3060 OEM run?
1115 of 2118 indexed open-weight models fit a GeForce RTX 3060 OEM at 8,192 context with f16 KV cache, the largest being Crow-9B-HERETIC-4.6 at I1-Q3_K_L. That covers text, vision-language, image, video and speech models.
How much usable memory does a GeForce RTX 3060 OEM actually have?
Its nameplate is 6 GB, but about 5.58 GiB is available to a model once driver and compositor overhead is accounted for.
Is a GeForce RTX 3060 OEM fast for local AI?
Its memory bandwidth is 336 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.