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HARDWARE FIT CHECK • DeepSeek-V3 (671B MoE) + NVIDIA GeForce RTX 3060 (12GB)427.26 GB / 12 GB VRAM

Can I Run DeepSeek-V3 (671B MoE) on NVIDIA GeForce RTX 3060 (12GB)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running DeepSeek-V3 (671B MoE) (671B) on NVIDIA GeForce RTX 3060 (12GB) (12 GB).

✕ OOM427.26 GB (999%)0 t/s

Target Hardware & Model

interactive
Ultra-fast MoE generation matching closed frontier models at fraction of hardware cost
DeepSeek-V3 (671B MoE) on NVIDIA GeForce RTX 3060 (12GB)MoE (37B Active / 671B Total)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
427.26 GB
999% of hardware capacity (12 GB)
Est. Decode Speed
0 tok/s
37B active params @ 360 GB/s
Available Headroom
-415.26 GB
Needs +415.26 GB more VRAM
Memory Allocation & Headroom
427.26 GB allocated of 12 GB capacity
999% utilized
Weights: 402.6 GBMoE (37B Active)
KV: (32K): 9.82 GBMLA (-85%)
Overhead: 14.84 GB
Deficit: -415.26 GB
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).
MoE Memory vs Speed Architecture: While decode throughput scales with the active parameter count (37B parameters streamed per token), all 671B parameters must be resident in VRAM so the dynamic router can route tokens to any expert layer.
Multi-Head Latent Attention (MLA): DeepSeek compresses Key-Value projections into a low-rank latent vector, cutting KV memory footprint by ~85% compared to standard MHA/GQA.

Local Hardware Configurations & Pricing

Out of VRAM: High-Capacity Hardware Upgrades
High-bandwidth VRAM specs and current market prices
Multi-GPU Rig↑ Upgrade
48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP
$1,550Amazon Prime →
Consumer GPU
12 GB VRAM

NVIDIA GeForce RTX 3060 (12GB)

Entry-level budget card for 7B–14B Q4 models

360 GB/s170W TDP
$289Amazon Prime →
Apple Silicon↑ Upgrade
54 GB VRAM

Apple Mac Mini M4 Pro (64GB Unified Memory)

Silent 65W desktop running 32B–70B models with large KV context

273 GB/s65W TDP
$1,999Amazon Prime →
Apple Silicon↑ Upgrade
112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Mistral Large 2 123B & Llama 3.3 70B Q8_0 locally with 128K context

546 GB/s145W TDP
$3,499Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek-V3 (671B MoE) @ 427.26 GB VRAM)

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$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)
Rent Spot GPU →
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$2.49/hr (H100) – $19.92/hr (8x H100)
Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw1342 GB9.82 GB1399.54 GB (999%)0 t/sOOM
Q8_08.5 bpw711.26 GB9.82 GB746.72 GB (999%)0 t/sOOM
Q6_K6.56 bpw550.22 GB9.82 GB580.05 GB (999%)0 t/sOOM
Q5_K_M5.68 bpw476.41 GB9.82 GB503.65 GB (999%)0 t/sOOM
Q4_K_M4.8 bpw402.6 GB9.82 GB427.26 GB (999%)0 t/sOOM
FP44.25 bpw355.63 GB9.82 GB378.65 GB (999%)0 t/sOOM
IQ3_XS3.3 bpw275.11 GB9.82 GB295.31 GB (999%)0 t/sOOM

Export Full Architecture & Infrastructure Spec

Download an instant Markdown architecture spec with exact memory math, hardware benchmarks, and runtime CLI flags.

FAQ & Reference

Frequently Asked Questions: DeepSeek-V3 (671B MoE) on NVIDIA GeForce RTX 3060 (12GB)

How much VRAM does DeepSeek-V3 (671B MoE) need on NVIDIA GeForce RTX 3060 (12GB)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-V3 (671B MoE) requires 427.26 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek-V3 (671B MoE) achieve on NVIDIA GeForce RTX 3060 (12GB)?

Based on NVIDIA GeForce RTX 3060 (12GB)'s 360 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.