HARDWARE FIT CHECK โข DeepSeek-V4.1-Flash (552B Engram MoE) + NVIDIA GeForce RTX 4090 (24GB GDDR6X)345.59 GB / 24 GB VRAM
Can I Run DeepSeek-V4.1-Flash (552B Engram MoE) on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?
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 4090 (24GB GDDR6X) (24 GB).
โ Out of Memory (OOM) โ Exceeds Hardware VRAM345.59 GB (1440%)0 t/s
DeepSeek-V4.1-Flash (552B Engram MoE) on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
345.59 GB
1440% of hardware capacity (24 GB)Est. Decode Speed
0 tok/s
16B active params @ 1008 GB/sAvailable Headroom
-321.59 GB
Needs +321.59 GB more VRAMMemory Allocation & Headroom
345.59 GB allocated of 24 GB capacity
1440% utilized
Weights: 331.2 GB
KV: (32K): 2.05 GB
Overhead: 12.34 GB
Deficit: -321.59 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 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:552bโ ๏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. (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)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 | 1104 GB | 2.05 GB | 1145.44 GB (4773%) | 0 t/s | OOM |
| Q8_08.5 bpw | 585.12 GB | 2.05 GB | 608.4 GB (2535%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 452.64 GB | 2.05 GB | 471.28 GB (1964%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 391.92 GB | 2.05 GB | 408.44 GB (1702%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 331.2 GB | 2.05 GB | 345.59 GB (1440%) | 0 t/s | OOM |
| FP44.25 bpw | 292.56 GB | 2.05 GB | 305.6 GB (1273%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 226.32 GB | 2.05 GB | 237.04 GB (988%) | 0 t/s | OOM |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
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
$1,550Amazon Prime โ
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8Bโ14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime โ
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