Intel · consumer

Arc A550M 8GB

Arc A550M 8GB has 8 GB of VRAM at 224 GB/s — about 7.44 GiB usable after driver and compositor overhead. 622 of 2118 indexed models fit at 64K context with f16 KV.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
8 GB
GDDR6
Bandwidth
224 GB/s
128-bit bus
Tensor FP16
—
dense
TDP
80 W
Context4K8K16K32K64K128K
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 470vision language 71embedding 19video 8audio asr 35image 1audio tts 18

What fits at 64K context

largest quantization that fits, per model · 622 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
OmniAtlas-Qwen3-30B-A3BI1-IQ1_M31.7B6.59 GiB0.00 GiB7.44 GiB0.00 GiB18±30%
Qwen3-Omni-30B-A3B-CaptionerI1-IQ1_M31.7B6.59 GiB0.00 GiB7.44 GiB0.00 GiB18±30%
granite-3.3-2b-instructQ5_K_S2.5B1.64 GiB5.00 GiB7.44 GiB0.00 GiB18±30%
granite-3.2-2b-instructQ5_K_S2.5B1.64 GiB5.00 GiB7.44 GiB0.00 GiB18±30%
granite-vision-3.2-2bQ5_K_S3.0B1.64 GiB5.00 GiB7.44 GiB0.00 GiB18±30%
SmolLM3-3BQ5_K_L3.1B2.12 GiB4.50 GiB7.43 GiB0.01 GiB18±30%
Vero-Qwen35-9B-BaseI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Vero-Qwen35-9BI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-Claude-4.6-Opus-Deckard-V4.2-Uncensored-Heretic-ThinkingI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Morphos-9BI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwable-9B-Claude-Fable-5-hereticI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Holo-3.1-9BI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwable-9B-Claude-Fable-5I1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-imabari-v2I1-Q3_K_L9.7B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-abliterated-v2-MAXI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
OmniCoder-9B-Claude-Opus-High-Reasoning-DistillI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwable-9B-Claude-Fable-5-StraTAI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwable-9B-Claude-Fable-5-OBLITERATEDI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-RpRMax-v1I1-Q3_K_L9.7B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
AdQWENistrator-9BI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
cajal-9b-v2-fullI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-ultra-uncensored-hereticQ3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Holo-3.1-9B-CoderI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
PlutoI1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Holo-3.1-9B-abliterated-rdoI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-Uncensored-cyber-v3Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-BaseI1-Q3_K_L9.7B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
qwen3.5-9b-nsfw-captioning-v5I1-Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Miss_MARTHA-9B-Qwen3.5-OmniI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-DS-v4-Flash-v3.0Q3_K_L9.4B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-DeepSeek-V4-FlashI1-Q3_K_L9.7B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Huihui-Qwen3.5-9B-Claude-4.6-Opus-abliteratedI1-Q3_K_L9.7B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwopus3.5-9B-v3.5Q3_K_L9.7B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Katarau-9B-ru-RP-nsfwI1-Q3_K_L9.0B4.59 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
granite-3.1-2b-instructQ5_02.5B1.62 GiB5.00 GiB7.42 GiB0.02 GiB18±30%
Fara1.5-9BQ3_K_M9.4B4.58 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
QwenPaw-Flash-9BQ3_K_M9.4B4.58 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
grug-9bQ3_K_M9.4B4.58 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
OmniCoder-9BQ3_K_M9.4B4.58 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Ornith-1.0-9BQ3_K_M9.2B4.58 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
Qwen3.5-9B-NeoQ3_K_M9.7B4.58 GiB2.00 GiB7.42 GiB0.02 GiB18±30%
LFM2-24B-A2BMoEIQ2_XXS23.8B5.35 GiB1.25 GiB7.41 GiB0.03 GiB35±37%
Dolphin3.0-Llama3.2-1BF321.2B4.61 GiB2.00 GiB7.41 GiB0.03 GiB18±30%
glm-4v-9bQ5_K_M13.9B6.57 GiB0.00 GiB7.41 GiB0.03 GiB18±30%
Parable-Granite-4.1-3B-Claude-Fable-5I1-Q3_K_M3.4B1.61 GiB5.00 GiB7.41 GiB0.03 GiB18±30%
granite-4.0-microQ3_K_M3.4B1.61 GiB5.00 GiB7.41 GiB0.03 GiB18±30%
granite-4.1-3bQ3_K_M3.4B1.61 GiB5.00 GiB7.41 GiB0.03 GiB18±30%
granite-4.0-micro-baseQ3_K_M3.4B1.61 GiB5.00 GiB7.41 GiB0.03 GiB18±30%
Qwen2.5-3B-Instruct-abliteratedI1-Q5_K_M3.1B4.34 GiB2.25 GiB7.41 GiB0.03 GiB18±30%
granite-4.0-1bQ8_01.6B1.62 GiB5.00 GiB7.40 GiB0.04 GiB18±30%
starcoder2-7bKV unresolvedQ2_K7.2B2.54 GiB4.00 GiB7.39 GiB0.05 GiB18±30%
Nanbeige4.1-3BQ5_K_S3.9B2.58 GiB4.00 GiB7.39 GiB0.05 GiB18±30%
InternVL3_5-8BQ6_K_L8.5B6.54 GiB0.00 GiB7.39 GiB0.05 GiB18±30%
nomic-embed-codeQ3_K_S7.1B3.03 GiB3.50 GiB7.39 GiB0.05 GiB18±30%
HunyuanVideo-1.5Q6_K8.3B6.54 GiB0.00 GiB7.39 GiB0.05 GiB18±30%
internlm3-8b-instructIQ3_XS8.8B3.56 GiB3.00 GiB7.38 GiB0.06 GiB18±30%
bge-reranker-v2-m3Q8_0568M0.59 GiB6.00 GiB7.37 GiB0.07 GiB18±30%
snowflake-arctic-embed-l-v2.0Q8_0568M0.59 GiB6.00 GiB7.37 GiB0.07 GiB18±30%
bge-m3Q8_0567M0.59 GiB6.00 GiB7.37 GiB0.07 GiB18±30%
Qwythos-9B-v2IQ3_M9.7B4.53 GiB2.00 GiB7.36 GiB0.08 GiB18±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 A550M 8GB run?
622 of 2118 indexed open-weight models fit a Arc A550M 8GB at 65,536 context with f16 KV cache, the largest being OmniAtlas-Qwen3-30B-A3B at I1-IQ1_M. That covers text, vision-language, image, video and speech models.
How much usable memory does a Arc A550M 8GB 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 Arc A550M 8GB fast for local AI?
Its memory bandwidth is 224 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.
Arc A550M 8GB — what AI models can it run locally? — ossmodeldb