DeepSeek V4 Flash: how much VRAM it needs and which GPU runs it

DeepSeek V4 Flash in UD-Q4_K_XL with an 8k context takes 145.5 GB of GPU memory. The cheapest option right now is 2× A800 PCIe 80 GB, from $1.98/h.

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VRAM by quantisation

The weight file and the total GPU memory with the context cache. One GPU, one request.

Quantisation	File	8K context	32K context	128K context	Fits on
FP4/FP8native	148.7	149.7	150.0	151.2	B200 180 GB
BF16	≈541.9	543.0	543.3	544.4	2+ GPUs
UD-Q4_K_XL	144.4	145.5	145.8	147.0	B200 180 GB
Q3_K_M	120.4	121.5	121.8	123.0	H200 NVL 141 GB
UD-Q2_K_XL	90.2	91.3	91.6	92.7	H100 NVL 94 GB

Context cache

How much memory the cache takes per request in FP16. A Q8 cache is about half. 12 KB per token.

8K context: 0.10 GB
32K context: 0.38 GB
128K context: 1.5 GB

Cheapest GPUs that run it

GPUs from the catalog the model fits on, by price per hour.

2× A800 PCIe 80 GB94% usedfrom $1.98/hRent
2× A100 PCIe 80 GB94% usedfrom $2.38/hRent
8× RTX 3090 24 GB81% usedfrom $2.51/hRent
8× RTX 4090 24 GB81% usedfrom $2.80/hRent
2× A100 SXM4 80 GB94% usedfrom $3.02/hRent

The price is the cheapest GPU of the model × the GPU count. A 2–8 GPU server can cost a little more per GPU.

How to run it on gpu.nz

For an API under load there is the vLLM template with an OpenAI-compatible API.

Questions

How much VRAM does DeepSeek V4 Flash need?: In UD-Q4_K_XL with an 8k context, 145.5 GB: weights 144.4 GB, context cache 0.10 GB and about 1 GB of runtime memory. In BF16, 543.0 GB.
Does DeepSeek V4 Flash run on an RTX 4090 with 24 GB?: It does not fit on one RTX 4090 even in UD-Q4_K_XL: it needs 145.5 GB. A 180 GB GB card or several RTX 4090s will do.
How much does it cost to run DeepSeek V4 Flash?: The cheapest fitting server right now is 2× A800 PCIe 80 GB, from $1.98/h. Billing is per minute: stop the server and only its disk is billed.
Which quantisation should I pick?: Q4_K_M is the chat default: close to the original quality at a third of BF16's size. Q5_K_M and Q6_K are more accurate, Q8_0 is almost the original. For vLLM use FP8 or BF16.

File sizes checked against Hugging Face on 2026-10-19. 1 GB here is 1024³ bytes, like GPU memory. Hugging Face lists decimal gigabytes, so its numbers are 7% larger.

VRAM by quantisation

The weight file and the total GPU memory with the context cache. One GPU, one request.

Quantisation	File	8K context	32K context	128K context	Fits on
FP4/FP8native	148.7	149.7	150.0	151.2	B200 180 GB
BF16	≈541.9	543.0	543.3	544.4	2+ GPUs
UD-Q4_K_XL	144.4	145.5	145.8	147.0	B200 180 GB
Q3_K_M	120.4	121.5	121.8	123.0	H200 NVL 141 GB
UD-Q2_K_XL	90.2	91.3	91.6	92.7	H100 NVL 94 GB

Cheapest GPUs that run it

GPUs from the catalog the model fits on, by price per hour.

2× A800 PCIe 80 GB94% usedfrom $1.98/hRent

2× A100 PCIe 80 GB94% usedfrom $2.38/hRent

8× RTX 3090 24 GB81% usedfrom $2.51/hRent

8× RTX 4090 24 GB81% usedfrom $2.80/hRent

2× A100 SXM4 80 GB94% usedfrom $3.02/hRent

The price is the cheapest GPU of the model × the GPU count. A 2–8 GPU server can cost a little more per GPU.

Questions

How much VRAM does DeepSeek V4 Flash need?

In UD-Q4_K_XL with an 8k context, 145.5 GB: weights 144.4 GB, context cache 0.10 GB and about 1 GB of runtime memory. In BF16, 543.0 GB.

Does DeepSeek V4 Flash run on an RTX 4090 with 24 GB?

It does not fit on one RTX 4090 even in UD-Q4_K_XL: it needs 145.5 GB. A 180 GB GB card or several RTX 4090s will do.

How much does it cost to run DeepSeek V4 Flash?

The cheapest fitting server right now is 2× A800 PCIe 80 GB, from $1.98/h. Billing is per minute: stop the server and only its disk is billed.

Which quantisation should I pick?

Q4_K_M is the chat default: close to the original quality at a third of BF16's size. Q5_K_M and Q6_K are more accurate, Q8_0 is almost the original. For vLLM use FP8 or BF16.