HARDWARE FIT CHECK • DeepSeek-V4.1-Flash (552B Engram MoE) + AMD Radeon RX 7900 XT (20GB GDDR6)345.59 GB / 20 GB VRAM

Can I Run DeepSeek-V4.1-Flash (552B Engram MoE) on AMD Radeon RX 7900 XT (20GB GDDR6)?

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 AMD Radeon RX 7900 XT (20GB GDDR6) (20 GB).

✕ OOM345.59 GB (1728%)0 t/s

DeepSeek-V4.1-Flash (552B Engram MoE) on AMD Radeon RX 7900 XT (20GB GDDR6)

✕ Out of Memory — Exceeds VRAM Capacity

✕ OOM

Required VRAM

345.59 GB

1728% of hardware capacity (20 GB)

Est. Decode Speed

0 tok/s

16B active params @ 800 GB/s

Available Headroom

-325.59 GB

Needs +325.59 GB more VRAM

Memory Allocation & Headroom

345.59 GB allocated of 20 GB capacity

1728% utilized

Weights: 331.2 GB

KV: (32K): 2.05 GB

Overhead: 12.34 GB

Deficit: -325.59 GB

⚡ Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Radeon RX 7900 XT (20GB GDDR6) (800 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)

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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

20 GB VRAM

AMD Radeon RX 7900 XT (20GB GDDR6)

Sub-$700 GPU with 20GB VRAM fitting 14B Q8 and 27B Q4 models

800 GB/s315W TDP

$699Amazon 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)

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Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Fit Status
FP1616 bpw	1104 GB	2.05 GB	1145.44 GB (5727%)	OOM
Q8_08.5 bpw	585.12 GB	2.05 GB	608.4 GB (3042%)	OOM
Q6_K6.56 bpw	452.64 GB	2.05 GB	471.28 GB (2356%)	OOM
Q5_K_M5.68 bpw	391.92 GB	2.05 GB	408.44 GB (2042%)	OOM
Q4_K_M4.8 bpw	331.2 GB	2.05 GB	345.59 GB (1728%)	OOM
FP44.25 bpw	292.56 GB	2.05 GB	305.6 GB (1528%)	OOM
IQ3_XS3.3 bpw	226.32 GB	2.05 GB	237.04 GB (1185%)	OOM

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FAQ & Reference

Frequently Asked Questions: DeepSeek-V4.1-Flash (552B Engram MoE) on AMD Radeon RX 7900 XT (20GB GDDR6)

How much VRAM does DeepSeek-V4.1-Flash (552B Engram MoE) need on AMD Radeon RX 7900 XT (20GB GDDR6)?

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 AMD Radeon RX 7900 XT (20GB GDDR6)?

Based on AMD Radeon RX 7900 XT (20GB GDDR6)'s 800 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.