Can DeepSeek LLM 67B run on H100 NVL 188GB?

YES — Runs Great

B59Good

○Estimated from fit model

DeepSeek LLM 67B needs ~66.4 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~155 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 66.4 GB, 168.1 tok/s, Runs well

66.4 GB required188.0 GB available

35% VRAM used

Fit status

Runs well

Decode

168.1 tok/s

TTFT

1152 ms

Safe context

Memory

66.4 GB / 188.0 GB

Memory breakdown

Weights40.9 GB

KV Cache5.8 GB

Runtime0.9 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 67B on H100 NVL 188GB
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: 168.1 tok/s decode · 1.2s TTFT (warm) · 420 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	168.1 tok/s	628 ms	4K
Coding	B	Runs well	154.6 tok/s	1252 ms	4K
Agentic Coding	B	Runs well	168.1 tok/s	1675 ms	4K
Reasoning	B	Runs well	168.1 tok/s	1361 ms	4K
RAG	B	Runs well	168.1 tok/s	2094 ms	4K

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.1 GB	Low	C48
Q3_K_S	3	32.8 GB	Low	C49
NVFP4	4

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

Frequently asked questions

See all results for H100 NVL 188GB See all hardware for DeepSeek LLM 67B

URL: https://willitrunai.com/can-run/deepseek-llm-67b-on-h100-nvl-188gb

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