NVIDIA · consumer

GeForce RTX 5060 Laptop

GeForce RTX 5060 Laptop has 8 GB of VRAM at 384 GB/s — about 7.44 GiB usable after driver and compositor overhead. 986 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
GDDR7
Bandwidth
384 GB/s
128-bit bus
Tensor FP16
—
dense
TDP
100 W
Context4K8K16K32K64K128K
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 806embedding 25vision language 88audio tts 20video 8audio asr 38image 1

What fits at 32K context

largest quantization that fits, per model · 986 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
OLMoE-1B-7B-0924-InstructMoEI1-IQ3_XS6.9B2.67 GiB4.00 GiB7.44 GiB0.00 GiB35±37%
Qwen3-Reranker-4BIQ4_XS4.0B2.13 GiB4.50 GiB7.44 GiB0.00 GiB41±12.9%
Octen-Embedding-4BIQ4_XS4.0B2.13 GiB4.50 GiB7.44 GiB0.00 GiB41±12.9%
OmniAtlas-Qwen3-30B-A3BI1-IQ1_M31.7B6.59 GiB0.00 GiB7.44 GiB0.00 GiB41±12.9%
Qwen3-Omni-30B-A3B-CaptionerI1-IQ1_M31.7B6.59 GiB0.00 GiB7.44 GiB0.00 GiB41±12.9%
Qwen2-1.5BF161.5B5.76 GiB0.88 GiB7.44 GiB0.00 GiB41±12.9%
Yi-Coder-1.5B-ChatIQ3_XS1.5B0.65 GiB6.00 GiB7.44 GiB0.00 GiB41±12.9%
Yi-Coder-1.5BIQ3_XS1.5B0.65 GiB6.00 GiB7.44 GiB0.00 GiB41±12.9%
next-8bI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Supertron2-Reranker-8BI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
next-ocrI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-8B-GLM-4.7-Flash-Heretic-Uncensored-ThinkingI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Midas-FableAgent-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-8B-Heretic-1.3.0I1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-8B-Thinking-Unredacted-MAXI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-8B-Instruct-Minecraft-MT-en-zhI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen-3-VL-8B-Instruct-hereticI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Poe-8B-GLM5-Opus4.6-Sonnet4.5-Kimi-Grok-Gemini-3-pro-preview-HERETICI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
ToolCUA-8BI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Huihui-Qwen3-VL-8B-Instruct-abliteratedI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-Reranker-8BI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Salience-1-9BI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-8B-Instruct-Uncensored-V2I1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Maestro1-9BI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
GRaPE-2-FlashI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Jan-v2-VL-medI1-IQ1_M8.8B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Parable-Qwen3-8B-Claude-Fable-5I1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
ReasonCritic-7BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
mythos-9b-unhinged-hereticI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Finch-8B-KTOI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Finch-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
MathSmith-hc-Qwen3-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
MiroThinker-v1.0-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
mythos-9b-unhingedI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Ektome-Qwen3-8B-PristinelyUncensoredI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Marco-DeepResearch-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
mythos-9b-mergedI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
qwen3-8b-apostateI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Josiefied-Qwen3-8B-abliterated-v1I1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
tmax-8bI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-8B-abliteratedI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
story_generation_Qwen3_8B_RLI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
AReaL-boba-2-8B-OpenI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
DS-R1-Qwen3-8B-ArliAI-RpR-v4-SmallI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
S1-Base-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Huihui-Qwen3-8B-abliterated-v2I1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Step3-VL-10B-BaseI1-IQ1_M10.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-Reranker-8BI1-IQ1_M8.2B2.10 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Dolphin3.0-Qwen2.5-1.5BF321.5B5.76 GiB0.88 GiB7.43 GiB0.01 GiB41±12.9%
SmolLM2-1.7B-InstructQ2_K1.7B0.63 GiB6.00 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-4B-Instruct-Unredacted-MAXI1-IQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-4B-ThinkingIQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-4B-Thinking-Unredacted-MAXI1-IQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Zubr1.0-VL-4BI1-IQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Huihui-Qwen3-VL-4B-Instruct-abliteratedI1-IQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-4B-Instruct-UncensoredI1-IQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Qwen3-VL-4B-InstructIQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
OpenCaption-4B-VL-SFT-v1.0I1-IQ4_XS4.4B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Parable-Qwen3-4B-Claude-Fable-5I1-IQ4_XS4.0B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±12.9%
Jan-v1-4BIQ4_XS4.0B2.11 GiB4.50 GiB7.43 GiB0.01 GiB41±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.

Measured on this card

third-party benchmarks, aggregated
Workload◍MedianMiddle 50%Runs
Image generation7.14 it/s5.22–8.3530
Benchmarked· n=30

Aggregated from community-submitted runs, so the spread is wide by nature — it covers different models, resolutions, step counts and settings, not one controlled configuration. Read the middle 50% rather than the median alone. These figures are reproduced with attribution from vladmandic-sd-data-benchmark, which publishes no licence — so we display and link rather than redistribute them.

Questions people ask

What AI models can a GeForce RTX 5060 Laptop run?
986 of 2118 indexed open-weight models fit a GeForce RTX 5060 Laptop at 32,768 context with f16 KV cache, the largest being OLMoE-1B-7B-0924-Instruct at I1-IQ3_XS. That covers text, vision-language, image, video and speech models.
How much usable memory does a GeForce RTX 5060 Laptop 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 GeForce RTX 5060 Laptop fast for local AI?
Its memory bandwidth is 384 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.
GeForce RTX 5060 Laptop — what AI models can it run locally? — ossmodeldb