NVIDIA GeForce RTX 4060 Laptop GPU
8 GB decides what fits. 256 GB/s decides how fast it runs once it does.
Open in the calculator →Best models for 8 GB, on every card that size →
Find one: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.
- Largest popular model that fitsOlmo-3-7B-Instruct7.3B params · Q4_K_M · needs 7.96 GB~25 tok/s About reading paceOpen in the calculator →
- Best fast pickGLM-4.6V-Flash10B params · Q4_K_M · needs 7.45 GB~30 tok/s Faster than you readOpen in the calculator →
- Most downloaded that fitsQwen3.5-9B9.7B params · Q4_K_M · needs 7.05 GB~36 tok/s Faster than you readOpen in the calculator →
At 8,192 tokens of context with the whole model in device memory. Speeds are estimates from memory bandwidth, not benchmarks run on this card.
130 of 320 fit entirely
130 of the 320 models the engine can size fit entirely in device memory at Q4_K_M where it is published, otherwise the nearest published format, and 8,192 tokens. 87 run with some layers on system memory (32 GB assumed), and 103 do not run at all.
Featured models · Q4_K_M at 8,192 tokens, including offload
| Model | Needs | Verdict | Decode | Calculator |
|---|---|---|---|---|
| Qwen3.5-2B2.3B parameters | 2.32 GB | fits | ~141 tok/s | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters | 3.46 GB | fits | ~65 tok/s | Open → |
| Qwen3-VL-8B-Instruct8.8B parameters | 7.04 GB | fits | ~30 tok/sAbout reading pace | Open → |
| Qwen3.5-9B9.7B parameters | 7.05 GB | fits | ~36 tok/s | Open → |
| gemma-4-12B-it12B parameters | 9.54 GB | 10 of 48 layers on system RAM (1.64 GB) | ~16 tok/s with offload | Open → |
| gpt-oss-20b21B parameters | 13.8 GB | 11 of 24 layers on system RAM (5.84 GB) | ~39 tok/s with offload | Open → |
| Qwen3.8-27B28B parameters | 18.6 GB | 40 of 64 layers on system RAM (10.7 GB) | ~5.5 tok/s with offload | Open → |
| Qwen3.6-27B28B parameters | 18.6 GB | 40 of 64 layers on system RAM (10.7 GB) | ~5.5 tok/s with offload | Open → |
| Kimi-Linear-48B-A3B-Instruct49B parameters | 30.4 GB | 21 of 27 layers on system RAM (22.9 GB) | ~39 tok/s with offload | Open → |
| Qwen3.8-Flash-Next180B parameters | 111.6 GB | needs 105.9 GB of system RAM; 32 GB assumed | does not run | Open → |
| DeepSeek-V4-Flash-Vision-Exp305B parameters | 188.1 GB | needs 182.9 GB of system RAM; 32 GB assumed | does not run | Open → |
| GLM-5.3-Flash321B parameters | 198.3 GB | needs 192.9 GB of system RAM; 32 GB assumed | does not run | Open → |
These examples are selected from prominent labs using the catalogue’s latest Hugging Face 30-day downloads and repository-creation freshness signal, with newer releases guaranteed a place. Offloaded rows assume 32 GB of system RAM, and a speed is only shown for a row that runs. Fitting in memory is not the same as loading: whether the runtime and version you have supports each architecture and format on this machine has not been tested here. Each “Open” link carries the same model, format, context and RAM into the calculator.
Fits entirely in NVIDIA GeForce RTX 4060 Laptop GPU memory — 130 of 320 sized models
The most demanding model that fits is Olmo-3-7B-Instruct at Q4_K_M: 7.96 GB of the 8 GB, leaving 0.04 GB spare.
Fully resident at 8,192 tokens (or the model’s own maximum, where that is shorter), offload off, at 256 GB/s. Each row is a run of the engine for this configuration; the rows start with current, prominent releases and “fits” is memory, not a tested runtime. Older or less prominent models remain available through this search and “Show all”.
Showing 40 of 130 models that fit.
| Model | Format | Needs | Spare | Decode | Downloads / 30d | Calculator |
|---|---|---|---|---|---|---|
| Qwen3.5-9BQwen · 9.7B params | Q4_K_M | 7.05 GB | 0.95 GB | ~36 tok/s | 9M | Open → |
| Qwen3.5-4BQwen · 4.7B params | Q4_K_M | 3.98 GB | 4.02 GB | ~64 tok/s | 7.8M | Open → |
| Qwen3.5-2BQwen · 2.3B params | Q4_K_M | 2.32 GB | 5.68 GB | ~141 tok/s | 4.9M | Open → |
| gemma-4-E4B-itGoogle · 8.0B params | Q4_K_M | 5.86 GB | 2.14 GB | ~58 tok/s | 4.4M | Open → |
| NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params | Q4_K_M | 3.46 GB | 4.54 GB | ~65 tok/s | 3.4M | Open → |
| gemma-4-E2B-itGoogle · 5.1B params | Q4_K_M | 4.02 GB | 3.98 GB | ~152 tok/s | 3M | Open → |
| Qwen3.5-0.8BQwen · 873M params | Q4_K_M | 1.46 GB | 6.54 GB | ~309 tok/s | 2.6M | Open → |
| Qwen3-VL-8B-InstructQwen · 8.8B params | Q4_K_M | 7.04 GB | 0.96 GB | ~30 tok/sAbout reading pace | 14.6M | Open → |
| North-Micro-Vision-InstructCohereLabs · 2.5B params | Q4_K_M | 2.99 GB | 5.01 GB | ~93 tok/s | 180.7K | Open → |
| granite-4.2-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 0.51 GB | ~27 tok/sAbout reading pace | 128.4K | Open → |
| granite-4.1-3bIBM · 3.4B params | Q4_K_M | 3.72 GB | 4.28 GB | ~63 tok/s | 521.8K | Open → |
| LFM2.5-2.6BLiquidAI · 2.7B params | Q4_K_M | 2.61 GB | 5.39 GB | ~101 tok/s | 108.1K | Open → |
| Qwen3-VL-4B-InstructQwen · 4.4B params | Q4_K_M | 4.72 GB | 3.28 GB | ~47 tok/s | 3.5M | Open → |
| Qwen3-VL-2B-InstructQwen · 2.1B params | Q4_K_M | 3.05 GB | 4.95 GB | ~85 tok/s | 2.8M | Open → |
| granite-4.2-3bIBM · 3.7B params | Q4_K_M | 3.72 GB | 4.28 GB | ~63 tok/s | 50.6K | Open → |
| LFM2.5-230MLiquidAI · 230M params | Q4_K_M | 1.05 GB | 6.95 GB | ~703 tok/s | 88.6K | Open → |
| Qwen3-0.6BQwen · 752M params | Q4_K_M | 2.22 GB | 5.78 GB | ~131 tok/s | 29.7M | Open → |
| granite-4.1-8bIBM · 8.8B params | Q4_K_M | 7.49 GB | 0.51 GB | ~27 tok/sAbout reading pace | 179.3K | Open → |
| LFM2.5-VL-3BLiquidAI · 3.1B params | Q4_K_M | 2.85 GB | 5.15 GB | ~101 tok/s | 26.7K | Open → |
| MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params | Q4_K_M | 6.90 GB | 1.10 GB | ~36 tok/s | 14.6K | Open → |
| Qwen3-4B-Instruct-2507Qwen · 4.0B params | Q4_K_M | 4.72 GB | 3.28 GB | ~47 tok/s | 3.7M | Open → |
| Ling-3.0-tinyinclusionAI · 7.9B params | Q4_K_M | 5.85 GB | 2.15 GB | ~203 tok/s | 17.7K | Open → |
| Qwen3-8BQwen · 8.2B params | Q4_K_M | 7.04 GB | 0.96 GB | ~30 tok/sAbout reading pace | 10.7M | Open → |
| Olmo-3-7B-Instructallenai · 7.3B params | Q4_K_M | 7.96 GB | 0.04 GB | ~25 tok/sAbout reading pace | 481.7K | Open → |
| Qwen3-4BQwen · 4.0B params | Q4_K_M | 4.51 GB | 3.49 GB | ~47 tok/s | 7.8M | Open → |
| LFM2.5-8B-A1BLiquidAI · 8.5B params | Q4_K_M | 6.06 GB | 1.94 GB | ~157 tok/s | 32.4K | Open → |
| LFM2.5-350MLiquidAI · 354M params | Q4_K_M | 1.13 GB | 6.87 GB | ~453 tok/s | 69.9K | Open → |
| Hy-MT2-1.8BTencent · 2.0B params | Q4_K_M | 2.47 GB | 5.53 GB | ~104 tok/s | 28.9K | Open → |
| LFM2.5-1.2B-InstructLiquidAI · 1.2B params | Q4_K_M | 1.63 GB | 6.37 GB | ~167 tok/s | 119.2K | Open → |
| Qwen3-1.7BQwen · 2.0B params | Q4_K_M | 3.02 GB | 4.98 GB | ~85 tok/s | 3.1M | Open → |
| Nemotron-3.5-Content-SafetyNVIDIA · 4.3B params | Q4_K_M | 3.73 GB | 4.27 GB | ~63 tok/s | 14.1K | Open → |
| Hy-MT2-7BTencent · 8.0B params | Q4_K_M | 6.50 GB | 1.50 GB | ~31 tok/s | 15.4K | Open → |
| GLM-4.6V-Flashzai-org · 10B params | Q4_K_M | 7.45 GB | 0.55 GB | ~30 tok/s | 103.7K | Open → |
| Qwen2.5-VL-7B-InstructQwen · 8.3B params | Q4_K_M | 6.36 GB | 1.64 GB | ~36 tok/s | 5.8M | Open → |
| SmolLM3-3BHuggingFaceTB · 3.1B params | Q4_K_M | 3.46 GB | 4.54 GB | ~69 tok/s | 617K | Open → |
| Ministral-3-8B-Instruct-2512Mistral AI · 8.9B params | Q4_K_M | 7.14 GB | 0.86 GB | ~29 tok/sAbout reading pace | 121.2K | Open → |
| Ministral-3-3B-Instruct-2512Mistral AI · 3.8B params | Q4_K_M | 3.82 GB | 4.18 GB | ~58 tok/s | 114.1K | Open → |
| NVIDIA-Nemotron-Nano-9B-v2NVIDIA · 8.9B params | Q4_K_M | 6.54 GB | 1.46 GB | ~30 tok/s | 333.6K | Open → |
| Qwen3-VL-8B-ThinkingQwen · 8.8B params | Q4_K_M | 7.04 GB | 0.96 GB | ~30 tok/sAbout reading pace | 144.2K | Open → |
| DeepSeek-R1-0528-Qwen3-8BDeepSeek · 8.2B params | Q4_K_M | 7.04 GB | 0.96 GB | ~30 tok/sAbout reading pace | 694.9K | Open → |
Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →
Find the NVIDIA GeForce RTX 4060 Laptop GPU: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.
The RTX 4060 Laptop, model by model
One page per model: whether it fits this device, at which formats, and how fast.
The specification behind every figure
What the manufacturer publishes for this device, and the pages it was read from.
Manufacturer specification
| Memory scope | dedicated |
|---|---|
| Capacity | 8 GB |
| Published options | 8 GB |
| Bandwidth | 256 GB/s |
| Memory type | GDDR6 |
| Bus width | 128-bit |
| FP32 peak | Not published |
| Dense matrix peak | Not published without sparsity |
| Power | Not published |
Source ledger
Caveats
- NVIDIA does not publish this card's memory bandwidth any more, so the figure comes from the TechPowerUp GPU Database (https://www.techpowerup.com/gpu-specs/geforce-rtx-4060-mobile.c3946), a third-party source, and is labelled as an estimate rather than a published specification.
- A laptop GPU's power limit is set by each laptop maker, which moves compute and so time to first token; decoding is limited by memory bandwidth, which the power limit changes far less. No power figure is recorded because there is no single one.
- No dense matrix throughput is published for this device in a form this catalogue accepts, so no compute roof is priced and time to first token is withheld.
Published capacity is a hardware ceiling, not guaranteed free runtime memory. The calculator shows the runtime reserve separately rather than folding it into a single number.
Run the diagnostic on the NVIDIA GeForce RTX 4060 Laptop GPU →