Can DeepSeek LLM 67B run on NVIDIA A100 40GB?

Q: What should I upgrade first if DeepSeek LLM 67B feels slow on NVIDIA A100 40GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

C50Usable

Estimated from fit model

DeepSeek LLM 67B needs ~43.5 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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.

DeepSeek LLM 67B at Q4_K_M needs 51.6 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q3_K_S (43.5 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 51.6 GB, exceeds 40.0 GB available

51.6 GB required40.0 GB available

129% VRAM needed

11.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.3 tok/s

TTFT

12678 ms

Safe context

Memory

51.6 GB / 40.0 GB

Offload

20%

Memory breakdown

Weights40.9 GB

KV Cache5.8 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek LLM 67B on NVIDIA A100 40GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 15.3 tok/s decode · 12.7s TTFT (warm) · 38 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	17.2 tok/s	6122 ms	4K
Coding	F	Too heavy	14.0 tok/s	13787 ms	4K
Agentic Coding	F	Too heavy	12.2 tok/s	23077 ms	4K
Reasoning	F	Too heavy	15.3 tok/s	14983 ms	4K
RAG	F	Too heavy	12.2 tok/s	28846 ms	4K

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	26.1 GB	Low	B58
Q3_K_S	3	32.8 GB	Low	F0

Get started

Copy-paste commands to run DeepSeek LLM 67B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
  --hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs DeepSeek LLM 67B well

RTX A6000 48GBBest value

48 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.9.7 tok/s decode

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

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBNVIDIA upgrade

48 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.19.2 tok/s decode

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

Raises estimated decode speed by about 25%.

~$4,999 MSRP

NVIDIA A16 64GBBudget pick

64 GB VRAM (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.12.5 tok/s decode

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.

~$6,500 MSRP

NVIDIA H100 80GBBiggest leap

80 GB VRAM (+40)3350 GB/s (+1795)

Makes the model fit on the accelerator instead of staying completely out of reach.74.9 tok/s decode

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.

~$40,000 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run DeepSeek LLM 67B?

Yes, NVIDIA A100 40GB can run DeepSeek LLM 67B at Q3_K_S quantization (Very compromised (needs ~2.7 GB host RAM)). The recommended Q4_K_M requires 51.6 GB which exceeds available memory, but at Q3_K_S it needs only 43.5 GB. Expected decode speed: 25.3 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 51.6 GB at Q4_K_M quantization. On NVIDIA A100 40GB, it fits at Q3_K_S using 43.5 GB.

What is the best quantization for DeepSeek LLM 67B?

The recommended quantization is Q4_K_M, but on NVIDIA A100 40GB the best fitting quantization is Q3_K_S, which uses 43.5 GB.

What speed will DeepSeek LLM 67B run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, DeepSeek LLM 67B achieves approximately 25.3 tokens per second decode speed with a time-to-first-token of 7664ms using Q3_K_S quantization.

Can NVIDIA A100 40GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on NVIDIA A100 40GB receives a F grade with 14.0 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, DeepSeek LLM 67B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for DeepSeek LLM 67B

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