HARDWARE BENCHMARK & VRAM ENVELOPE โข CONSUMER GPU12 GB VRAM โข 360 GB/s
What Models Can Run on NVIDIA GeForce RTX 3060 (12GB)?
12 GB VRAM360 GB/s Bandwidth170W TDPMSRP: $289Cloud: $0.18/hr on Vast.ai
NVIDIA GeForce RTX 3060 (12GB) provides 12 GB of usable memory with 360 GB/s memory bandwidth at 170W TDP. Entry-level budget card for 7Bโ14B Q4 models.
โ Out of Memory (OOM) โ Exceeds Hardware VRAM21.93 GB (183%)0 t/s
Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 3060 (12GB)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
21.93 GB
183% of hardware capacity (12 GB)Est. Decode Speed
0 tok/s
Dense model @ 360 GB/sAvailable Headroom
-9.93 GB
Needs +9.93 GB more VRAMMemory Allocation & Headroom
21.93 GB allocated of 12 GB capacity
183% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Deficit: -9.93 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3060 (12GB) (360 GB/s). Prompt prefill / TTFT (~0 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen3.8:27bโ ๏ธ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Qwen 3.8 27B Instruct @ 21.93 GB VRAM)
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 54.4 GB | 4.29 GB | 61.34 GB (511%) | 0 t/s | OOM |
| Q8_08.5 bpw | 28.83 GB | 4.29 GB | 34.88 GB (291%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 22.3 GB | 4.29 GB | 28.12 GB (234%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 19.31 GB | 4.29 GB | 25.03 GB (209%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 16.32 GB | 4.29 GB | 21.93 GB (183%) | 0 t/s | OOM |
| FP44.25 bpw | 14.42 GB | 4.29 GB | 19.96 GB (166%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 11.15 GB | 4.29 GB | 16.58 GB (138%) | 0 t/s | OOM |
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All Open-Weights Models Tested on NVIDIA GeForce RTX 3060 (12GB) (Q4_K_M @ 32K Context)
Standard Q4_K_M precision with 32K context window and Q8_0 KV cache
| Model | Architecture | Total VRAM Needed | Headroom | Est. Decode Speed | Compatibility | Detailed Config |
|---|---|---|---|---|---|---|
| Qwen 3.8 27B Instruct Alibaba Cloud | 27.2B Dense | 21.93 GB(183%) | -9.93 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Qwen 3.6 35B-A3B MoE Alibaba Cloud | 35B MoE | 24.1 GB(201%) | -12.1 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| DeepSeek-V4.1-Flash (552B Engram MoE) DeepSeek | 552B MoE | 345.59 GB(2880%) | -333.59 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Meta Muse Spark Open (816B-A42B MoE) Meta AI | 816B MoE | 512.86 GB(4274%) | -500.86 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Google Gemma 4 26B MoE Google | 26.4B MoE | 20.23 GB(169%) | -8.23 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Google Gemma 4 E4B (Edge 4B) Google | 4.1B Dense | 4.24 GB(35%) | +7.76 GB | ~87 tok/s | โ FITS | Configure CLI โ |
| DeepSeek-R1 Distill Qwen 32B DeepSeek | 32.8B Dense | 25.41 GB(212%) | -13.41 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| DeepSeek-R1 Distill Qwen 14B DeepSeek | 14.8B Dense | 13.16 GB(110%) | -1.16 GB | ~5 tok/s | โก PARTIAL | Configure CLI โ |
| Meta Llama 4 Scout (109B-A17B MoE) Meta AI | 109B MoE | 71.66 GB(597%) | -59.66 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Meta Llama 3.3 70B Instruct Meta AI | 70.6B Dense | 49.96 GB(416%) | -37.96 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Meta Llama 3.1 8B Instruct Meta AI | 8B Dense | 7.87 GB(66%) | +4.13 GB | ~44 tok/s | โ FITS | Configure CLI โ |
| Microsoft Phi-4 Mini 3.8B Microsoft | 3.8B Dense | 3.92 GB(33%) | +8.08 GB | ~94 tok/s | โ FITS | Configure CLI โ |