Deploy GPU โ†—
HARDWARE FIT CHECK โ€ข DeepSeek-V4.1-Flash (552B Engram MoE) + NVIDIA GeForce RTX 3060 (12GB)345.59 GB / 12 GB VRAM

Can I Run DeepSeek-V4.1-Flash (552B Engram 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-V4.1-Flash (552B Engram MoE) (552B) on NVIDIA GeForce RTX 3060 (12GB) (12 GB).

โŒ Out of Memory (OOM) โ€” Exceeds Hardware VRAM345.59 GB (2880%)0 t/s

Model & Target Hardware

interactive
Frontier SWE Coding (77.0 DeepSWE), 1M Context & 3D/Game Synthesis
DeepSeek-V4.1-Flash (552B Engram MoE) on NVIDIA GeForce RTX 3060 (12GB)

โœ• Out of Memory โ€” Exceeds VRAM Capacity

โœ• OOM
Required VRAM
345.59 GB
2880% of hardware capacity (12 GB)
Est. Decode Speed
0 tok/s
16B active params @ 360 GB/s
Available Headroom
-333.59 GB
Needs +333.59 GB more VRAM
Memory Allocation & Headroom
345.59 GB allocated of 12 GB capacity
2880% utilized
Weights: 331.2 GB
KV: (32K): 2.05 GB
Overhead: 12.34 GB
Deficit: -333.59 GB
โšก 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 deepseek-v4.1-flash:552b

โš ๏ธ 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-V4.1-Flash (552B Engram MoE) @ 345.59 GB VRAM)

RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ€“ $2.39/hr (H100)
Launch 1-Click GPU Pod โ†’ โ†’
Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ€“ $0.55/hr (RTX 4090)
Rent Spot GPU โ†’ โ†’
Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)
Check H100 Availability โ†’ โ†’

Quantization Precision Matrix

32K ctx โ€ข KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw1104 GB2.05 GB1145.44 GB (9545%)0 t/sOOM
Q8_08.5 bpw585.12 GB2.05 GB608.4 GB (5070%)0 t/sOOM
Q6_K6.56 bpw452.64 GB2.05 GB471.28 GB (3927%)0 t/sOOM
Q5_K_M5.68 bpw391.92 GB2.05 GB408.44 GB (3404%)0 t/sOOM
Q4_K_M4.8 bpw331.2 GB2.05 GB345.59 GB (2880%)0 t/sOOM
FP44.25 bpw292.56 GB2.05 GB305.6 GB (2547%)0 t/sOOM
IQ3_XS3.3 bpw226.32 GB2.05 GB237.04 GB (1975%)0 t/sOOM

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices
Consumer GPU12 GB VRAM

NVIDIA GeForce RTX 3060 (12GB)

Entry-level budget card for 7Bโ€“14B Q4 models

360 GB/s170W TDP
Consumer GPU32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27Bโ€“35B models & Blackwell FP4 quantization

1792 GB/s575W TDP
Consumer GPU24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14Bโ€“32B Q4_K_M models

1008 GB/s450W TDP
Multi-GPU Rig48 GB VRAM

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

Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP

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