Intel · workstation

Arc Pro A50 6GB

Arc Pro A50 6GB has 6 GB of VRAM at 192 GB/s — about 5.58 GiB usable after driver and compositor overhead. 441 of 2118 indexed models fit at 64K context with f16 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
6 GB
GDDR6
Bandwidth
192 GB/s
96-bit bus
Tensor FP16
—
dense
TDP
75 W
Context4K8K16K32K64K128K
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 333vision language 44audio asr 30audio tts 17video 3embedding 14

What fits at 64K context

largest quantization that fits, per model · 441 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
Huihui-gemma-3n-E4B-it-abliteratedQ4_07.8B3.81 GiB0.93 GiB5.58 GiB0.00 GiB22±30%
DeepSeek-OCR-2MoEQ8_03.4B2.91 GiB1.88 GiB5.58 GiB0.00 GiB25±37%
DeepSeek-OCRMoEQ8_03.3B2.91 GiB1.88 GiB5.58 GiB0.00 GiB25±37%
Qwen2.5-3B-Instruct-abliteratedI1-IQ3_XXS3.1B2.51 GiB2.25 GiB5.58 GiB0.00 GiB22±30%
Holo-3.1-4BI1-IQ4_NL5.2B2.76 GiB2.00 GiB5.58 GiB0.00 GiB22±30%
AfriqueQwen3.5-4BI1-IQ4_NL5.2B2.76 GiB2.00 GiB5.58 GiB0.00 GiB22±30%
TimeOmni-1-4BI1-IQ4_NL5.2B2.76 GiB2.00 GiB5.58 GiB0.00 GiB22±30%
Hunyuan-1.8B-InstructIQ3_XS1.8B0.78 GiB4.00 GiB5.58 GiB0.00 GiB21±30%
canary-qwen-2.5bBF162.6B4.73 GiB0.00 GiB5.57 GiB0.01 GiB22±30%
G9v3-3BQ3_K_L3.0B1.53 GiB3.25 GiB5.57 GiB0.01 GiB21±30%
EXAONE-Deep-7.8BQ4_K_L7.8B4.73 GiB0.00 GiB5.57 GiB0.01 GiB22±30%
EXAONE-3.5-7.8B-InstructQ4_K_L7.8B4.73 GiB0.00 GiB5.57 GiB0.01 GiB22±30%
Qwen3-TTS-12Hz-0.6B-BaseQ4_K_M915M4.72 GiB0.00 GiB5.57 GiB0.01 GiB22±30%
VoxCPM2F162.3B4.72 GiB0.00 GiB5.57 GiB0.01 GiB22±30%
MiniCPM-V-4Q6_K4.1B2.76 GiB2.00 GiB5.57 GiB0.01 GiB22±30%
gemma-2-2b-it-abliteratedQ2_K_L2.6B1.28 GiB3.48 GiB5.57 GiB0.01 GiB22±30%
t5-v1_1-xxlQ2_K4.8B4.72 GiB0.00 GiB5.56 GiB0.02 GiB22±30%
granite-4.0-7B-A1B-Creative-v0.1MoEI1-Q5_K_S6.7B4.30 GiB0.50 GiB5.56 GiB0.02 GiB48±37%
Teuken-7B-instruct-research-v0.4I1-IQ2_XXS7.5B2.72 GiB2.00 GiB5.56 GiB0.02 GiB22±30%
Vikhr-Gemma-2B-instructIQ3_S2.6B1.27 GiB3.48 GiB5.56 GiB0.02 GiB22±30%
Gemmasutra-Mini-2B-v1I1-IQ3_S2.6B1.27 GiB3.48 GiB5.56 GiB0.02 GiB22±30%
gemma-2-2b-itQ3_K_S2.6B1.27 GiB3.48 GiB5.56 GiB0.02 GiB22±30%
gemma-4-E4B-itQ3_K_M8.0B3.78 GiB0.94 GiB5.54 GiB0.04 GiB22±30%
EXAONE-4.0-1.2B-abliteratedI1-Q5_K_M1.5B1.00 GiB3.75 GiB5.53 GiB0.05 GiB22±30%
ToriiGate-0.5Q4_K_S5.2B2.72 GiB2.00 GiB5.53 GiB0.05 GiB22±30%
chandra-ocr-2Q4_K_S5.3B2.72 GiB2.00 GiB5.53 GiB0.05 GiB22±30%
Darwin-4B-ChimeraI1-Q5_K_M4.0B2.69 GiB2.03 GiB5.53 GiB0.05 GiB22±30%
InternVL3_5-8BQ4_K_M8.5B4.68 GiB0.00 GiB5.53 GiB0.05 GiB22±30%
GLM-ASR-Nano-2512Q4_K2.3B1.23 GiB3.50 GiB5.53 GiB0.05 GiB22±30%
gemma-3n-E2B-itQ6_K5.4B3.92 GiB0.80 GiB5.52 GiB0.06 GiB22±30%
LFM2.5-8B-A1BMoEUD-IQ4_XS8.5B3.97 GiB0.75 GiB5.52 GiB0.06 GiB39±37%
Qwen3.5-9B-BaseTQ1_09.7B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Vero-Qwen35-9B-BaseI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Vero-Qwen35-9BI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwen3.5-9B-Claude-4.6-Opus-Deckard-V4.2-Uncensored-Heretic-ThinkingI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Morphos-9BI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwable-9B-Claude-Fable-5-hereticI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Holo-3.1-9BI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwable-9B-Claude-Fable-5I1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwen3.5-9B-imabari-v2I1-IQ1_M9.7B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwen3.5-9B-abliterated-v2-MAXI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
OmniCoder-9B-Claude-Opus-High-Reasoning-DistillI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwable-9B-Claude-Fable-5-StraTAI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwable-9B-Claude-Fable-5-OBLITERATEDI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwen3.5-9B-RpRMax-v1I1-IQ1_M9.7B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
AdQWENistrator-9BI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
cajal-9b-v2-fullI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Holo-3.1-9B-CoderI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
PlutoI1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Holo-3.1-9B-abliterated-rdoI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
qwen3.5-9b-nsfw-captioning-v5I1-IQ1_M9.4B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Miss_MARTHA-9B-Qwen3.5-OmniI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Qwen3.5-9B-DeepSeek-V4-FlashI1-IQ1_M9.7B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Huihui-Qwen3.5-9B-Claude-4.6-Opus-abliteratedI1-IQ1_M9.7B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
Katarau-9B-ru-RP-nsfwI1-IQ1_M9.0B2.68 GiB2.00 GiB5.52 GiB0.06 GiB22±30%
MARTHA-LXVII.8B_QWEN-3.5-3.6_prune_9b-3.6_baseI1-IQ2_S8.1B2.92 GiB1.75 GiB5.51 GiB0.07 GiB22±30%
gemma-4-E2B-itQ6_K_L5.1B4.25 GiB0.46 GiB5.51 GiB0.07 GiB22±30%
Fara1.5-4BQ4_K_M4.5B2.69 GiB2.00 GiB5.50 GiB0.08 GiB22±30%
AREX-TurboQ4_K_M4.5B2.69 GiB2.00 GiB5.50 GiB0.08 GiB22±30%
Dolphin3.0-Qwen2.5-3bQ6_K_L3.1B2.43 GiB2.25 GiB5.50 GiB0.08 GiB22±30%
●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 Arc Pro A50 6GB run?
441 of 2118 indexed open-weight models fit a Arc Pro A50 6GB at 65,536 context with f16 KV cache, the largest being Huihui-gemma-3n-E4B-it-abliterated at Q4_0. That covers text, vision-language, image, video and speech models.
How much usable memory does a Arc Pro A50 6GB 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 Arc Pro A50 6GB fast for local AI?
Its memory bandwidth is 192 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.