Can DeepSeek LLM 7B run on GTX 1080 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek LLM 7B needs ~13.6 GB but GTX 1080 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.6 GB, exceeds 8.0 GB available

13.6 GB required8.0 GB available

170% VRAM needed

5.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.1 tok/s

TTFT

19232 ms

Safe context

Memory

13.6 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek LLM 7B on GTX 1080 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 10.1 tok/s decode · 19.2s TTFT (warm) · 25 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.6 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	20.1 tok/s	5265 ms	4K
Coding	F	Too heavy	10.1 tok/s	19232 ms	4K
Agentic Coding	F	Too heavy	6.6 tok/s	42459 ms	4K
Reasoning	F	Too heavy	10.1 tok/s	22728 ms	4K
RAG	F	Too heavy	6.6 tok/s	53073 ms	4K

Inference speed

DeepSeek LLM 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek LLM 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Tight
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	48.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	30.2	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	11.4	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C52
Q3_K_S	3	3.4 GB	Low	C52
NVFP4	4	3.9 GB	Medium	C52
Q4_K_M	4	4.3 GB	Medium	C52
Q5_K_MBest for your GPU	5	5.0 GB	High	C51
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

アップグレードオプション

DeepSeek LLM 7Bを快適に動かすハードウェア

RTX 3060 12GBコスパ最良

12 GB VRAM (+4)360 GB/s (+40)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 199%.

〜$329 MSRP

RTX 5060 Ti 16GBコスパ重視

16 GB VRAM (+8)448 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$449 MSRP

RTX 4060 Ti 16GBNVIDIAアップグレード

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$499 MSRP

Frequently asked questions

Can GTX 1080 8GB run DeepSeek LLM 7B?

No, DeepSeek LLM 7B requires more memory than GTX 1080 8GB provides.

How much VRAM does DeepSeek LLM 7B need?

DeepSeek LLM 7B (7B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 7B?

The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 7B run at on GTX 1080 8GB?

On GTX 1080 8GB, DeepSeek LLM 7B achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19232ms using Q4_K_M quantization.

Can GTX 1080 8GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on GTX 1080 8GB receives a F grade with 10.1 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on GTX 1080 8GB?

On GTX 1080 8GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek LLM 7B feels slow on GTX 1080 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1080 8GB See all hardware for DeepSeek LLM 7B

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