Deploy GPU ↗
HARDWARE FIT CHECK • DeepSeek-V4.1-Flash (552B Engram MoE) + NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)345.59 GB / 16 GB VRAM

Can I Run DeepSeek-V4.1-Flash (552B Engram MoE) on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?

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 5070 Ti (16GB GDDR7) (16 GB).

✕ OOM345.59 GB (2160%)0 t/s

Model & Target Hardware

interactive
Frontier SWE Coding (77.0 DeepSWE), 1M Context & 3D/Game Synthesis
GPU Topology / Quantity:Single GPU / Unified SoC
DeepSeek-V4.1-Flash (552B Engram MoE) on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM
Required VRAM
345.59 GB
2160% of hardware capacity (16 GB)
Est. Decode Speed
0 tok/s
16B active params @ 896 GB/s
Available Headroom
-329.59 GB
Needs +329.59 GB more VRAM
Memory Allocation & Headroom
345.59 GB allocated of 16 GB capacity
2160% utilized
Weights: 331.2 GB
KV: (32K): 2.05 GB
Overhead: 12.34 GB
Deficit: -329.59 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7) (896 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

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 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP
$1,550Amazon Prime →
Apple Silicon↑ Upgrade
112 GB VRAM

Apple Mac Studio M4 Max (128GB Unified Memory)

Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context

546 GB/s145W TDP
$3,499Amazon Prime →
Consumer GPU
16 GB VRAM

NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)

Next-gen 16GB Blackwell GPU for fast 14B–32B Q4_K_M coding

896 GB/s300W TDP
$749Amazon 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-V4.1-Flash (552B Engram MoE) @ 345.59 GB VRAM)

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

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw1104 GB2.05 GB1145.44 GB (7159%)0 t/sOOM
Q8_08.5 bpw585.12 GB2.05 GB608.4 GB (3803%)0 t/sOOM
Q6_K6.56 bpw452.64 GB2.05 GB471.28 GB (2946%)0 t/sOOM
Q5_K_M5.68 bpw391.92 GB2.05 GB408.44 GB (2553%)0 t/sOOM
Q4_K_M4.8 bpw331.2 GB2.05 GB345.59 GB (2160%)0 t/sOOM
FP44.25 bpw292.56 GB2.05 GB305.6 GB (1910%)0 t/sOOM
IQ3_XS3.3 bpw226.32 GB2.05 GB237.04 GB (1482%)0 t/sOOM

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FAQ & Reference

Frequently Asked Questions: DeepSeek-V4.1-Flash (552B Engram MoE) on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)

How much VRAM does DeepSeek-V4.1-Flash (552B Engram MoE) need on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-V4.1-Flash (552B Engram MoE) requires 345.59 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek-V4.1-Flash (552B Engram MoE) achieve on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)?

Based on NVIDIA GeForce RTX 5070 Ti (16GB GDDR7)'s 896 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.